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3dpcp/.svn/pristine/ab/abd41dce84d3a5c2f03fd95ad30c160b542192de.svn-base

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update to svn r762
2012-11-13 08:12:22 +00:00
/*
* veloslam implementation
*
* Copyright (C) Andreas Nuechter, Li Wei, Li Ming
*
* Released under the GPL version 3.
*
*/
/**
* @file
* @brief Main programm for dynamic Velodyne SLAM
*
* @author Andreas Nuechter. Jacobs University Bremen, Germany
* @author Li Wei, Wuhan University, China
* @author Li Ming, Wuhan University, China
*/
#ifdef _MSC_VER
#ifdef OPENMP
#define _OPENMP
#endif
#endif
#ifdef _OPENMP
#include <omp.h>
#endif
#ifdef _MSC_VER
#include <windows.h>
#else
#include <dlfcn.h>
#endif
#include "slam6d/icp6Dapx.h"
#include "slam6d/icp6Dsvd.h"
#include "slam6d/icp6Dquat.h"
#include "slam6d/icp6Dortho.h"
#include "slam6d/icp6Dhelix.h"
#include "slam6d/icp6Ddual.h"
#include "slam6d/icp6Dlumeuler.h"
#include "slam6d/icp6Dlumquat.h"
#include "slam6d/icp6Dquatscale.h"
#include "slam6d/icp6D.h"
#ifdef WITH_CUDA
#include "slam6d/cuda/icp6Dcuda.h"
#endif
#include "slam6d/lum6Deuler.h"
#include "slam6d/lum6Dquat.h"
#include "slam6d/ghelix6DQ2.h"
#include "slam6d/elch6Deuler.h"
#include "slam6d/elch6Dquat.h"
#include "slam6d/elch6DunitQuat.h"
#include "slam6d/elch6Dslerp.h"
#include "slam6d/graphSlam6D.h"
#include "slam6d/gapx6D.h"
#include "slam6d/graph.h"
#include "slam6d/globals.icc"
#ifndef _MSC_VER
#include <getopt.h>
#else
#include "XGetopt.h"
#endif
#include <csignal>
#include "veloslam/veloscan.h"
#include "veloslam/debugview.h"
#include "veloslam/tracker.h"
#include "veloslam/trackermanager.h"
#include "veloslam/intersection_detection.h"
#ifdef _MSC_VER
#define strcasecmp _stricmp
#define strncasecmp _strnicmp
#else
#include <strings.h>
#endif
#ifdef WITH_METRICS
#include "slam6d/metrics.h"
#endif //WITH_METRICS
#include <GL/gl.h> /* OpenGL header file */
#include <GL/glu.h> /* OpenGL utilities header file */
#ifdef _MSC_VER
#include <GL/glut.h>
#else
#include <GL/freeglut.h>
#endif
#ifndef _MSC_VER
#include <time.h>
void Sleep(unsigned int mseconds)
{
clock_t goal = mseconds + clock();
while (goal > clock());
}
#endif
#define WANT_STREAM ///< define the WANT stream :)
#include <string>
using std::string;
#include <iostream>
using std::cout;
using std::cerr;
using std::endl;
#include <fstream>
using std::ifstream;
#include <boost/thread.hpp>
#include <boost/thread/mutex.hpp>
#include <boost/thread/condition.hpp>
using namespace boost;
extern boost::mutex keymutex;
extern boost::condition keycond;
extern void StartShow();
extern TrackerManager trackMgr;
extern int sliding_window_size;
extern int current_sliding_window_pos;
extern Trajectory VelodyneTrajectory;
extern VeloScan* g_pfirstScan;
extern bool g_pause;
extern float constant_static_or_moving;
extern bool DebugDrawFinished ;
extern bool ICPFinished;
extern bool save_animation;
extern int anim_frame_rate;
extern int scanCount;
// Handling Segmentation faults and CTRL-C
void sigSEGVhandler (int v)
{
static bool segfault = false;
if(!segfault) {
segfault = true;
cout << endl
<< "# **************************** #" << endl
<< " Segmentation fault or Ctrl-C" << endl
<< "# **************************** #" << endl
<< endl;
// save frames and close scans
for(ScanVector::iterator it = Scan::allScans.begin(); it != Scan::allScans.end(); ++it) {
(*it)->saveFrames();
}
cout << "Frames saved." << endl;
Scan::closeDirectory();
}
exit(-1);
}
/**
* Explains the usage of this program's command line parameters
*/
void usage(char* prog)
{
#ifndef _MSC_VER
const string bold("\033[1m");
const string normal("\033[m");
#else
const string bold("");
const string normal("");
#endif
cout << endl
<< bold << "USAGE " << normal << endl
<< " " << prog << " [options] directory" << endl << endl;
cout << bold << "OPTIONS" << normal << endl
<< bold << " -a" << normal << " NR, " << bold << "--algo=" << normal << "NR [default: 1]" << endl
<< " selects the minimizazion method for the ICP matching algorithm" << endl
<< " 1 = unit quaternion based method by Horn" << endl
<< " 2 = singular value decomposition by Arun et al. " << endl
<< " 3 = orthonormal matrices by Horn et al." << endl
<< " 4 = dual quaternion method by Walker et al." << endl
<< " 5 = helix approximation by Hofer & Potmann" << endl
<< " 6 = small angle approximation" << endl
<< " 7 = Lu & Milios style, i.e., uncertainty based, with Euler angles" << endl
<< " 8 = Lu & Milios style, i.e., uncertainty based, with Quaternion" << endl
<< " 9 = unit quaternion with scale method by Horn" << endl
<< endl
<< bold << " -A" << normal << " NR, " << bold << "--anim=" << normal << "NR [default: first and last frame only]" << endl
<< " if specified, use only every NR-th frame for animation" << endl
<< endl
<< bold << " -c" << normal << " NR, " << bold << "--cldist=" << normal << "NR [default: 500]" << endl
<< " specifies the maximal distance for closed loops" << endl
<< endl
<< bold << " -C" << normal << " NR, " << bold << "--clpairs=" << normal << "NR [default: 6]" << endl
<< " specifies the minimal number of points for an overlap. If not specified" << endl
<< " cldist is used instead" << endl
<< endl
<< bold << " --cache" << normal << endl
<< " turns on cached k-d tree search" << endl
<< endl
<< bold << " -d" << normal << " NR, " << bold << "--dist=" << normal << "NR [default: 25]" << endl
<< " sets the maximal point-to-point distance for matching with ICP to <NR> 'units'" << endl
<< " (unit of scan data, e.g. cm)" << endl
<< endl
<< bold << " -D" << normal << " NR, " << bold << "--distSLAM="
<< normal << "NR [default: same value as -d option]" << endl
<< " sets the maximal point-to-point distance for matching with SLAM to <NR> 'units'" << endl
<< " (unit of scan data, e.g. cm)" << endl
<< endl
<< bold << " --DlastSLAM" << normal << " NR [default not set]" << endl
<< " sets the maximal point-to-point distance for the final SLAM correction," << endl
<< " if final SLAM is not required don't set it." << endl
<< endl
<< bold << " -e" << normal << " NR, " << bold << "--end=" << normal << "NR" << endl
<< " end after scan NR" << endl
<< endl
<< bold << " --exportAllPoints" << normal << endl
<< " writes all registered reduced points to the file points.pts before" << endl
<< " slam6D terminated" << endl
<< endl
<< bold << " --epsICP=" << normal << "NR [default: 0.00001]" << endl
<< " stop ICP iteration if difference is smaller than NR" << endl
<< endl
<< bold << " --epsSLAM=" << normal << " NR [default: 0.5]" << endl
<< " stop SLAM iteration if average difference is smaller than NR" << endl
<< endl
<< bold << " -f" << normal << " F, " << bold << "--format=" << normal << "F" << endl
<< " using shared library F for input" << endl
<< " (chose F from {uos, uos_map, uos_rgb, uos_frames, uos_map_frames, old, rts, rts_map, ifp, riegl_txt, riegl_rgb, riegl_bin, zahn, ply, wrl, xyz, zuf, iais, front, x3d, rxp, ais })" << endl
<< endl
<< bold << " -G" << normal << " NR, " << bold << "--graphSlam6DAlgo=" << normal << "NR [default: 0]" << endl
<< " selects the minimizazion method for the SLAM matching algorithm" << endl
<< " 0 = no global relaxation technique" << endl
<< " 1 = Lu & Milios extension using euler angles due to Borrmann et al." << endl
<< " 2 = Lu & Milios extension using using unit quaternions" << endl
<< " 3 = HELIX approximation by Hofer and Pottmann" << endl
<< " 4 = small angle approximation" << endl
<< endl
<< bold << " -i" << normal << " NR, " << bold << "--iter=" << normal << "NR [default: 50]" << endl
<< " sets the maximal number of ICP iterations to <NR>" << endl
<< endl
<< bold << " -I" << normal << " NR, " << bold << "--iterSLAM=" << normal << "NR [default: 0]" << endl
<< " sets the maximal number of iterations for SLAM to <NR>" << endl
<< " (if not set, graphSLAM is not executed)" << endl
<< endl
<< bold << " -l" << normal << " NR, " << bold << "--loopsize=" << normal << "NR [default: 20]" << endl
<< " sets the size of a loop, i.e., a loop must exceed <NR> of scans" << endl
<< endl
<< bold << " -L" << normal << " NR, " << bold << "--loop6DAlgo=" << normal << "NR [default: 0]" << endl
<< " selects the method for closing the loop explicitly" << endl
<< " 0 = no loop closing technique" << endl
<< " 1 = euler angles" << endl
<< " 2 = quaternions " << endl
<< " 3 = unit quaternions" << endl
<< " 4 = SLERP (recommended)" << endl
<< endl
<< bold << " --metascan" << normal << endl
<< " Match current scan against a meta scan of all previous scans (default match against the last scan only)" << endl
<< endl
<< bold << " -m" << normal << " NR, " << bold << "--max=" << normal << "NR" << endl
<< " neglegt all data points with a distance larger than NR 'units'" << endl
<< endl
<< bold << " -M" << normal << " NR, " << bold << "--min=" << normal << "NR" << endl
<< " neglegt all data points with a distance smaller than NR 'units'" << endl
<< endl
<< bold << " -n" << normal << " FILE, " << bold << "--net=" << normal << "FILE" << endl
<< " specifies the file that includes the net structure for SLAM" << endl
<< endl
<< bold << " -O" << normal << "NR (optional), " << bold << "--octree=" << normal << "NR (optional)" << endl
<< " use randomized octree based point reduction (pts per voxel=<NR>)" << endl
<< " requires " << bold << "-r" << normal <<" or " << bold << "--reduce" << endl
<< endl
<< bold << " -p, --trustpose" << normal << endl
<< " Trust the pose file, do not extrapolate the last transformation." << endl
<< " (just for testing purposes, or gps input.)" << endl
<< endl
<< bold << " -q, --quiet" << normal << endl
<< " Quiet mode. Suppress (most) messages" << endl
<< endl
<< bold << " -Q, --veryquiet" << normal << endl
<< " Very quiet mode. Suppress all messages, except in case of error." << endl
<< endl
<< bold << " -S, --scanserver" << normal << endl
<< " Use the scanserver as an input method and handling of scan data" << endl
<< endl
<< bold << " -r" << normal << " NR, " << bold << "--reduce=" << normal << "NR" << endl
<< " turns on octree based point reduction (voxel size=<NR>)" << endl
<< endl
<< bold << " -R" << normal << " NR, " << bold << "--random=" << normal << "NR" << endl
<< " turns on randomized reduction, using about every <NR>-th point only" << endl
<< endl
<< bold << " -s" << normal << " NR, " << bold << "--start=" << normal << "NR" << endl
<< " start at scan NR (i.e., neglects the first NR scans)" << endl
<< " [ATTENTION: counting naturally starts with 0]" << endl
<< endl
<< bold << " -t" << normal << " NR, " << bold << "--nns_method=" << normal << "NR [default: 1]" << endl
<< " selects the Nearest Neighbor Search Algorithm" << endl
<< " 0 = simple k-d tree " << endl
<< " 1 = cached k-d tree " << endl
<< " 2 = ANNTree " << endl
<< " 3 = BOCTree " << endl
<< endl
<< bold << " -u" << normal <<", "<< bold<<"--cuda" << normal << endl
<< " this option activates icp running on GPU instead of CPU"<<endl
<< endl << endl;
cout << bold << "EXAMPLES " << normal << endl
<< " " << prog << " dat" << endl
<< " " << prog << " --max=500 -r 10.2 -i 20 dat" << endl
<< " " << prog << " -s 2 -e 10 dat" << endl << endl;
exit(1);
}
/**
* Make type, start and end a write once only class for parseFormatFile because
* we know the directory only after the type, start and end parameters may have
* been written already, so encapsulate this write once behaviour in this class.
*/
template<typename T>
class WriteOnce {
public:
WriteOnce(T& value) : value(value), written(false) {}
WriteOnce& operator=(const T& other) { if(!written) { value = other; written = true; } return *this; }
operator T() const { return value; }
private:
T& value;
bool written;
};
/**
* Parsing of a formats file in the scan directory for default type and scan
* index ranges without overwriting user set parameters. Does nothing if
* file doesn't exist.
*
* @param dir directory the scans and format file are contained in
* @param type which ScanIO to use for the scans in that directory
* @param start index for the first valid scan
* @param end index for the last valid scan
*/
void parseFormatFile(string& dir, WriteOnce<IOType>& type, WriteOnce<int>& start, WriteOnce<int>& end)
{
ifstream file((dir+"format").c_str());
if(!file.good()) return;
string line, key, value, format;
while(getline(file, line)) {
size_t pos = line.find('=');
key = trim(line.substr(0, pos - 0));
value = trim(line.substr(pos+1));
if(key == "format") {
try {
format = value;
type = formatname_to_io_type(format.c_str());
} catch (...) { // runtime_error
cerr << "Error while parsing format file: Format '" << format << "' unknown." << endl;
break;
}
} else if(key == "start") {
stringstream str(value.c_str());
int s;
str >> s;
start = s;
} else if(key == "end") {
stringstream str(value.c_str());
int e;
str >> e;
end = e;
} else {
cerr << "Error while parsing format file: Unknown key '" << key << "'" << endl;
break;
}
}
}
/** A function that parses the command-line arguments and sets the respective flags.
* @param argc the number of arguments
* @param argv the arguments
* @param dir the directory
* @param red using point reduction?
* @param rand use randomized point reduction?
* @param mdm maximal distance match
* @param mdml maximal distance match for SLAM
* @param mni maximal number of iterations
* @param start starting at scan number 'start'
* @param end stopping at scan number 'end'
* @param maxDist - maximal distance of points being loaded
* @param minDist - minimal distance of points being loaded
* @param quiet switches on/off the quiet mode
* @param veryQuiet switches on/off the 'very quiet' mode
* @param extrapolate_pose - i.e., extrapolating the odometry by the last transformation
* (vs. taking the pose file as <b>exact</b>)
* @param meta match against all scans (= meta scan), or against the last scan only???
* @param anim selects the rotation representation for the matching algorithm
* @param mni_lum sets the maximal number of iterations for SLAM
* @param net specifies the file that includes the net structure for SLAM
* @param cldist specifies the maximal distance for closed loops
* @param epsilonICP stop ICP iteration if difference is smaller than this value
* @param epsilonSLAM stop SLAM iteration if average difference is smaller than this value
* @param algo specfies the used algorithm for rotation computation
* @param lum6DAlgo specifies the used algorithm for global SLAM correction
* @param loopsize defines the minimal loop size
* @param tracking select sematic algorithm of none/classification/tracking on/off the point classification mode
* @return 0, if the parsing was successful. 1 otherwise
*/
int parseArgs(int argc, char **argv, string &dir, double &red, int &rand,
double &mdm, double &mdml, double &mdmll,
int &mni, int &start, int &end, int &maxDist, int &minDist, bool &quiet, bool &veryQuiet,
bool &extrapolate_pose, bool &meta, int &algo,int &tracking, int &loopSlam6DAlgo, int &lum6DAlgo, int &anim,
int &mni_lum, string &net, double &cldist, int &clpairs, int &loopsize,int &trackingAlgo,
double &epsilonICP, double &epsilonSLAM, int &nns_method, bool &exportPts, double &distLoop,
int &iterLoop, double &graphDist, int &octree, bool &cuda_enabled, IOType &type,
bool& scanserver)
{
int c;
// from unistd.h:
extern char *optarg;
extern int optind;
WriteOnce<IOType> w_type(type);
WriteOnce<int> w_start(start), w_end(end);
/* options descriptor */
// 0: no arguments, 1: required argument, 2: optional argument
static struct option longopts[] = {
{ "format", required_argument, 0, 'f' },
{ "algo", required_argument, 0, 'a' },
{ "tracking", required_argument, 0, 'b' },
{ "track_value", required_argument, 0, 'T' },
{ "nns_method", required_argument, 0, 't' },
{ "loop6DAlgo", required_argument, 0, 'L' },
{ "graphSlam6DAlgo", required_argument, 0, 'G' },
{ "net", required_argument, 0, 'n' },
{ "iter", required_argument, 0, 'i' },
{ "iterSLAM", required_argument, 0, 'I' },
{ "max", required_argument, 0, 'm' },
{ "loopsize", required_argument, 0, 'l' },
{ "cldist", required_argument, 0, 'c' },
{ "clpairs", required_argument, 0, 'C' },
{ "min", required_argument, 0, 'M' },
{ "dist", required_argument, 0, 'd' },
{ "distSLAM", required_argument, 0, 'D' },
{ "start", required_argument, 0, 's' },
{ "end", required_argument, 0, 'e' },
{ "reduce", required_argument, 0, 'r' },
{ "octree", optional_argument, 0, 'O' },
{ "random", required_argument, 0, 'R' },
{ "quiet", no_argument, 0, 'q' },
{ "veryquiet", no_argument, 0, 'Q' },
{ "trustpose", no_argument, 0, 'p' },
{ "anim", required_argument, 0, 'A' },
{ "metascan", no_argument, 0, '2' }, // use the long format only
{ "DlastSLAM", required_argument, 0, '4' }, // use the long format only
{ "epsICP", required_argument, 0, '5' }, // use the long format only
{ "epsSLAM", required_argument, 0, '6' }, // use the long format only
{ "exportAllPoints", no_argument, 0, '8' },
{ "distLoop", required_argument, 0, '9' }, // use the long format only
{ "iterLoop", required_argument, 0, '1' }, // use the long format only
{ "graphDist", required_argument, 0, '3' }, // use the long format only
{ "cuda", no_argument, 0, 'u' }, // cuda will be enabled
{ "trackingAlgo", required_argument, 0, 'y'},//tracking algorithm
{ "scanserver", no_argument, 0, 'S' },
{ 0, 0, 0, 0} // needed, cf. getopt.h
};
cout << endl;
while ((c = getopt_long(argc, argv, "T:O:v:f:A:G:L:a:b:t:r:R:d:D:i:l:I:c:C:n:S:s:e:m:M:y:u:q:Q:p", longopts, NULL)) != -1)
switch (c)
{
case 'a':
algo = atoi(optarg);
if ((algo < 0) || (algo > 9)) {
cerr << "Error: ICP Algorithm not available." << endl;
exit(1);
}
break;
case 'b':
tracking = atoi(optarg);
break;
case 'v':
save_animation = true;
anim_frame_rate = atoi(optarg);
break;
case 'T':
constant_static_or_moving = atof(optarg);
if ((constant_static_or_moving < 0) || (constant_static_or_moving > 300)) {
cerr << "Error: constant_static_or_moving not available." << endl;
exit(1);
}
break;
case 't':
nns_method = atoi(optarg);
if ((nns_method < 0) || (nns_method > 3)) {
cerr << "Error: NNS Method not available." << endl;
exit(1);
}
break;
case 'L':
loopSlam6DAlgo = atoi(optarg);
if (loopSlam6DAlgo < 0 || loopSlam6DAlgo > 6) {
cerr << "Error: global loop closing algorithm not available." << endl;
exit(1);
}
break;
case 'G':
lum6DAlgo = atoi(optarg);
if ((lum6DAlgo < 0) || (lum6DAlgo > 6)) {
cerr << "Error: global relaxation algorithm not available." << endl;
exit(1);
}
break;
case 'c':
cldist = atof(optarg);
break;
case 'C':
clpairs = atoi(optarg);
break;
case 'l':
loopsize = atoi(optarg);
break;
case 'r':
red = atof(optarg);
break;
case 'O':
if (optarg) {
octree = atoi(optarg);
} else {
octree = 1;
}
break;
case 'R':
rand = atoi(optarg);
break;
case 'd':
mdm = atof(optarg);
break;
case 'D':
mdml = atof(optarg);
break;
case 'i':
mni = atoi(optarg);
break;
case 'I':
mni_lum = atoi(optarg);
break;
case 'n':
net = optarg;
break;
case 's':
w_start = atoi(optarg);
if (w_start < 0) { cerr << "Error: Cannot start at a negative scan number.\n"; exit(1); }
break;
case 'e':
w_end = atoi(optarg);
if (w_end < 0) { cerr << "Error: Cannot end at a negative scan number.\n"; exit(1); }
if (w_end < w_start) { cerr << "Error: <end> cannot be smaller than <start>.\n"; exit(1); }
break;
case 'm':
maxDist = atoi(optarg);
break;
case 'M':
minDist = atoi(optarg);
break;
case 'q':
quiet = true;
break;
case 'Q':
quiet = veryQuiet = true;
break;
case 'p':
extrapolate_pose = false;
break;
case 'y':
trackingAlgo=atoi(optarg);//choose tracking algorithm
break;
case 'A':
anim = atoi(optarg);
break;
case '2': // = --metascan
meta = true;
break;
case '4': // = --DlastSLAM
mdmll = atof(optarg);
break;
case '5': // = --epsICP
epsilonICP = atof(optarg);
break;
case '6': // = --epsSLAM
epsilonSLAM = atof(optarg);
break;
case '8': // not used
exportPts = true;
break;
case '9': // = --distLoop
distLoop = atof(optarg);
break;
case '1': // = --iterLoop
iterLoop = atoi(optarg);
break;
case '3': // = --graphDist
graphDist = atof(optarg);
break;
case 'f':
try {
w_type = formatname_to_io_type(optarg);
} catch (...) { // runtime_error
cerr << "Format " << optarg << " unknown." << endl;
abort();
}
break;
case 'u':
cuda_enabled = true;
break;
case 'S':
scanserver = true; // maybe some errors.
break;
case '?':
usage(argv[0]);
return 1;
default:
abort ();
}
scanserver = true;
if (optind != argc-1)
{
cerr << "\n*** Directory missing ***" << endl;
usage(argv[0]);
}
dir = argv[optind];
#ifndef _MSC_VER
if (dir[dir.length()-1] != '/') dir = dir + "/";
#else
if (dir[dir.length()-1] != '\\') dir = dir + "\\";
#endif
parseFormatFile(dir, w_type, w_start, w_end);
return 0;
}
/**
* This function is does all the matching stuff
* it iterates over all scans using the algorithm objects to calculate new poses
* objects could be NULL if algorithm should not be used
*
* @param cldist maximal distance for closing loops
* @param loopsize minimal loop size
* @param allScans Contains all laser scans
* @param my_icp6D the ICP implementation
* @param meta_icp math ICP against a metascan
* @param nns_method Indicates the nearest neigbor search method to be used
* @param my_loopSlam6D used loopoptimizer
* @param my_graphSlam6D used global optimization
* @param nrIt The number of iterations the global SLAM-algorithm will run
* @param epsilonSLAM epsilon for global SLAM iteration
* @param mdml maximal distance match for global SLAM
* @param mdmll maximal distance match for global SLAM after all scans ar matched
*/
void matchGraph6Dautomatic(double cldist, int loopsize, vector <Scan *> allScans, icp6D *my_icp6D,
bool meta_icp, int nns_method, bool cuda_enabled,
loopSlam6D *my_loopSlam6D, graphSlam6D *my_graphSlam6D, int nrIt,
double epsilonSLAM, double mdml, double mdmll, double graphDist,
bool &eP, IOType type)
{
double cldist2 = sqr(cldist);
// list of scan for metascan
vector < Scan* > metas;
// graph for loop optimization
graph_t g;
int n = allScans.size();
int loop_detection = 0;
double dist, min_dist = -1;
int first = 0, last = 0;
for(int i = 1; i < n; i++) {
cout << i << "/" << n << endl;
add_edge(i-1, i, g);
if(eP) {
allScans[i]->mergeCoordinatesWithRoboterPosition(allScans[i-1]);
}
//Hack to get all icp transformations into the .frames Files
if(i == n-1 && my_icp6D != NULL && my_icp6D->get_anim() == -2) {
my_icp6D->set_anim(-1);
}
/*if(i == 85 || i == 321 || i == 533) {
my_icp6D->set_anim(1);
}*/
if(my_icp6D != NULL){
cout << "ICP" << endl;
// Matching strongly linked scans with ICPs
if(meta_icp)
{
// metas.push_back(allScans[i - 1]);
// MetaScan* meta_scan = new MetaScan(metas);
// my_icp6D->match(meta_scan, allScans[i]);
//delete meta_scan;
} else {
switch(type) {
case UOS_MAP:
case UOS_MAP_FRAMES:
my_icp6D->match(allScans[0], allScans[i]);
break;
case RTS_MAP:
//untested (and could not work)
//if(i < 220-22 && i > 250-22) match(allScans[0], CurrentScan);
my_icp6D->match(allScans[0], allScans[i]);
break;
default:
my_icp6D->match(allScans[i - 1], allScans[i]);
break;
}
}
} else {
double id[16];
M4identity(id);
allScans[i]->transform(id, Scan::ICP, 0);
}
/*if(i == 85 || i == 321 || i == 533) {
my_icp6D->set_anim(-2);
}*/
if(loop_detection == 1) {
loop_detection = 2;
}
for(int j = 0; j < i - loopsize; j++) {
dist = Dist2(allScans[j]->get_rPos(), allScans[i]->get_rPos());
if(dist < cldist2) {
loop_detection = 1;
if(min_dist < 0 || dist < min_dist) {
min_dist = dist;
first = j;
last = i;
}
}
}
if(loop_detection == 2) {
loop_detection = 0;
min_dist = -1;
if(my_loopSlam6D != NULL) {
cout << "Loop close: " << first << " " << last << endl;
my_loopSlam6D->close_loop(allScans, first, last, g);
add_edge(first, last, g);
}
if(my_graphSlam6D != NULL && mdml > 0) {
int j = 0;
double ret;
do {
// recalculate graph
Graph *gr = new Graph(i + 1, cldist2, loopsize);
cout << "Global: " << j << endl;
ret = my_graphSlam6D->doGraphSlam6D(*gr, allScans, 1);
delete gr;
j++;
} while (j < nrIt && ret > epsilonSLAM);
}
}
}
if(loop_detection == 1 && my_loopSlam6D != NULL) {
cout << "Loop close: " << first << " " << last << endl;
my_loopSlam6D->close_loop(allScans, first, last, g);
add_edge(first, last, g);
}
if(my_graphSlam6D != NULL && mdml > 0.0) {
int j = 0;
double ret;
do {
// recalculate graph
Graph *gr = new Graph(n, cldist2, loopsize);
cout << "Global: " << j << endl;
ret = my_graphSlam6D->doGraphSlam6D(*gr, allScans, 1);
delete gr;
j++;
} while (j < nrIt && ret > epsilonSLAM);
}
if(my_graphSlam6D != NULL && mdmll > 0.0) {
my_graphSlam6D->set_mdmll(mdmll);
int j = 0;
double ret;
do {
// recalculate graph
Graph *gr = new Graph(n, sqr(graphDist), loopsize);
cout << "Global: " << j << endl;
ret = my_graphSlam6D->doGraphSlam6D(*gr, allScans, 1);
delete gr;
j++;
} while (j < nrIt && ret > epsilonSLAM);
}
}
icp6Dminimizer * CreateICPalgo( int algo , bool quiet )
{
icp6Dminimizer *my_icp6Dminimizer = 0;
switch (algo) {
case 1 :
my_icp6Dminimizer = new icp6D_QUAT(quiet);
break;
case 2 :
my_icp6Dminimizer = new icp6D_SVD(quiet);
break;
case 3 :
my_icp6Dminimizer = new icp6D_ORTHO(quiet);
break;
case 4 :
my_icp6Dminimizer = new icp6D_DUAL(quiet);
break;
case 5 :
my_icp6Dminimizer = new icp6D_HELIX(quiet);
break;
case 6 :
my_icp6Dminimizer = new icp6D_APX(quiet);
break;
case 7 :
my_icp6Dminimizer = new icp6D_LUMEULER(quiet);
break;
case 8 :
my_icp6Dminimizer = new icp6D_LUMQUAT(quiet);
break;
case 9 :
my_icp6Dminimizer = new icp6D_QUAT_SCALE(quiet);
break;
}
return my_icp6Dminimizer;
}
int FinalSLAM( double &red, int &rand,
double &mdm, double &mdml, double &mdmll,
int &mni, int &start, int &end, int &maxDist, int &minDist, bool &quiet, bool &veryQuiet,
bool &eP, bool &meta, int &algo, int &loopSlam6DAlgo, int &lum6DAlgo, int &anim,
int &mni_lum, string &net, double &cldist, int &clpairs, int &loopsize,
double &epsilonICP, double &epsilonSLAM, int &nns_method, bool &exportPts, double &distLoop,
int &iterLoop, double &graphDist, int &octree, bool &cuda_enabled, IOType &type
)
{
icp6Dminimizer *my_icp6Dminimizer = 0;
my_icp6Dminimizer= CreateICPalgo( algo, quiet);
if (mni_lum == -1 && loopSlam6DAlgo == 0)
{
icp6D *my_icp = 0;
if (cuda_enabled) {
#ifdef WITH_CUDA
my_icp = new icp6Dcuda(my_icp6Dminimizer, mdm, mni, quiet, meta, rand, eP,
anim, epsilonICP, nns_method, cuda_enabled);
#else
cout << "slam6d was not compiled for excuting CUDA code" << endl;
#endif
} else {
my_icp = new icp6D(my_icp6Dminimizer, mdm, mni, quiet, meta, rand, eP,
anim, epsilonICP, nns_method, cuda_enabled);
}
if (my_icp) my_icp->doICP(Scan::allScans);
delete my_icp;
} else if (clpairs > -1) {
//!!!!!!!!!!!!!!!!!!!!!!!!
icp6D *my_icp = 0;
if (cuda_enabled) {
#ifdef WITH_CUDA
my_icp = new icp6Dcuda(my_icp6Dminimizer, mdm, mni, quiet, meta, rand, eP,
anim, epsilonICP, nns_method, cuda_enabled);
#else
cout << "slam6d was not compiled for excuting CUDA code" << endl;
#endif
} else {
my_icp = new icp6D(my_icp6Dminimizer, mdm, mni, quiet, meta, rand, eP,
anim, epsilonICP, nns_method, cuda_enabled);
}
my_icp->doICP(Scan::allScans);
graphSlam6D *my_graphSlam6D = new lum6DEuler(my_icp6Dminimizer, mdm, mdml, mni, quiet, meta,
rand, eP, anim, epsilonICP, nns_method, epsilonSLAM);
my_graphSlam6D->matchGraph6Dautomatic(Scan::allScans, mni_lum, clpairs, loopsize);
//!!!!!!!!!!!!!!!!!!!!!!!!
} else {
graphSlam6D *my_graphSlam6D = 0;
switch (lum6DAlgo) {
case 1 :
my_graphSlam6D = new lum6DEuler(my_icp6Dminimizer, mdm, mdml, mni, quiet, meta, rand, eP,
anim, epsilonICP, nns_method, epsilonSLAM);
break;
case 2 :
my_graphSlam6D = new lum6DQuat(my_icp6Dminimizer, mdm, mdml, mni, quiet, meta, rand, eP,
anim, epsilonICP, nns_method, epsilonSLAM);
break;
case 3 :
my_graphSlam6D = new ghelix6DQ2(my_icp6Dminimizer, mdm, mdml, mni, quiet, meta, rand, eP,
anim, epsilonICP, nns_method, epsilonSLAM);
break;
case 4 :
my_graphSlam6D = new gapx6D(my_icp6Dminimizer, mdm, mdml, mni, quiet, meta, rand, eP,
anim, epsilonICP, nns_method, epsilonSLAM);
break;
}
// Construct Network
if (net != "none") {
icp6D *my_icp = 0;
if (cuda_enabled) {
#ifdef WITH_CUDA
my_icp = new icp6Dcuda(my_icp6Dminimizer, mdm, mni, quiet, meta, rand, eP,
anim, epsilonICP, nns_method);
#else
cout << "slam6d was not compiled for excuting CUDA code" << endl;
#endif
} else {
my_icp = new icp6D(my_icp6Dminimizer, mdm, mni, quiet, meta, rand, eP,
anim, epsilonICP, nns_method);
}
my_icp->doICP(Scan::allScans);
Graph* structure;
structure = new Graph(net);
my_graphSlam6D->doGraphSlam6D(*structure, Scan::allScans, mni_lum);
if(mdmll > 0.0) {
my_graphSlam6D->set_mdmll(mdmll);
my_graphSlam6D->doGraphSlam6D(*structure, Scan::allScans, mni_lum);
}
} else {
icp6D *my_icp = 0;
if(algo > 0) {
if (cuda_enabled) {
#ifdef WITH_CUDA
my_icp = new icp6Dcuda(my_icp6Dminimizer, mdm, mni, quiet, meta, rand, eP,
anim, epsilonICP, nns_method);
#else
cout << "slam6d was not compiled for excuting CUDA code" << endl;
#endif
} else {
my_icp = new icp6D(my_icp6Dminimizer, mdm, mni, quiet, meta, rand, eP,
anim, epsilonICP, nns_method);
}
loopSlam6D *my_loopSlam6D = 0;
switch(loopSlam6DAlgo) {
case 1:
my_loopSlam6D = new elch6Deuler(veryQuiet, my_icp6Dminimizer, distLoop, iterLoop,
rand, eP, 10, epsilonICP, nns_method);
break;
case 2:
my_loopSlam6D = new elch6Dquat(veryQuiet, my_icp6Dminimizer, distLoop, iterLoop,
rand, eP, 10, epsilonICP, nns_method);
break;
case 3:
my_loopSlam6D = new elch6DunitQuat(veryQuiet, my_icp6Dminimizer, distLoop, iterLoop,
rand, eP, 10, epsilonICP, nns_method);
break;
case 4:
my_loopSlam6D = new elch6Dslerp(veryQuiet, my_icp6Dminimizer, distLoop, iterLoop,
rand, eP, 10, epsilonICP, nns_method);
break;
}
matchGraph6Dautomatic(cldist, loopsize, Scan::allScans, my_icp, meta,
nns_method, cuda_enabled, my_loopSlam6D, my_graphSlam6D,
mni_lum, epsilonSLAM, mdml, mdmll, graphDist, eP, type);
delete my_icp;
if(loopSlam6DAlgo > 0) {
delete my_loopSlam6D;
}
}
if(my_graphSlam6D > 0) {
delete my_graphSlam6D;
}
}
}
delete my_icp6Dminimizer;
return 0;
}
void MatchTwoScan(icp6D *my_icp, VeloScan* currentScan, int scanCount, bool eP )
{
Scan *PreviousScan = 0;
//////////////////////ICP//////////////////////
if (scanCount > 0)
{
PreviousScan = Scan::allScans[scanCount-1];
// extrapolate odometry // <20><>ǰһ֡<D2BB><D6A1><EFBFBD><EFBFBD><EFBFBD><EFBFBD>Ϊ<EFBFBD><CEAA>׼
if (eP)
currentScan->mergeCoordinatesWithRoboterPosition(PreviousScan);
my_icp->match(PreviousScan, currentScan);
}
}
/**
* Main program for 6D SLAM.
* Usage: bin/slam6D 'dir',
* with 'dir' the directory of a set of scans
* ...
*/
int main(int argc, char **argv)
{
#ifndef _MSC_VER
glutInit(&argc, argv);
#else
glutInit(&argc, argv);
#endif
signal (SIGSEGV, sigSEGVhandler);
signal (SIGINT, sigSEGVhandler);
cout << "slam6D - A highly efficient SLAM implementation based on scan matching" << endl
<< " with 6 degrees of freedom" << endl
<< "(c) Jacobs University Bremen gGmbH, Germany, since 2009" << endl
<< " University of Osnabrueck, Germany, 2006 - 2009" << endl << endl;
if (argc <= 1) {
usage(argv[0]);
}
// parsing the command line parameters
// init, default values if not specified
string dir;
double red = -1.0, mdmll = -1.0, mdml = 25.0, mdm = 25.0;
int rand = -1, mni = 50;
int start = 0, end = -1;
bool quiet = false;
bool veryQuiet = false;
int maxDist = -1;
int minDist = -1;
bool eP = true; // should we extrapolate the pose??
bool meta = false; // match against meta scan, or against LAST scan only?
int algo = 1;
int mni_lum = -1;
double cldist = 500;
int clpairs = -1;
int loopsize = 20;
string net = "none";
int anim = -1;
double epsilonICP = 0.00001;
double epsilonSLAM = 0.5;
int nns_method = simpleKD;
bool exportPts = false;
int loopSlam6DAlgo = 0;
int lum6DAlgo = 0;
int tracking = 1;
double distLoop = 700.0;
int iterLoop = 100;
double graphDist = cldist;
int octree = 0; // employ randomized octree reduction?
bool cuda_enabled = false;
IOType type = UOS;
int trackingAlgo=0;
bool scanserver = false;
parseArgs(argc, argv, dir, red, rand, mdm, mdml, mdmll, mni, start, end,
maxDist, minDist, quiet, veryQuiet, eP, meta, algo, tracking,
loopSlam6DAlgo, lum6DAlgo, anim,
mni_lum, net, cldist, clpairs, loopsize, trackingAlgo,epsilonICP, epsilonSLAM,
nns_method, exportPts, distLoop, iterLoop, graphDist, octree, cuda_enabled, type,
scanserver);
cout << "VeloSLAM will proceed with the following parameters:" << endl;
//@@@ to do :-)
icp6Dminimizer *my_icp6Dminimizer = 0;
my_icp6Dminimizer= CreateICPalgo( algo, quiet);
icp6D *my_icp = 0;
my_icp = new icp6D(my_icp6Dminimizer, mdm, mni, quiet, meta, rand, eP,
anim, epsilonICP, nns_method, cuda_enabled);
if (my_icp==0)
{
cerr<< "can not create ICP " << endl;
exit(0);
}
Scan::openDirectory(scanserver, dir, type, start, end);
if(VeloScan::allScans.size() == 0) {
cerr << "No scans found. Did you use the correct format?" << endl;
exit(-1);
}
double eu[6] = {0.0, 0.0, 0.0, 0.0, 0.0, 0.0};
vector <Point> ptss;
veryQuiet =true;
if(!veryQuiet)
StartShow();
ICPFinished =true;
//Main Loop for ICP with Moving Object Detection and Tracking
for(ScanVector::iterator it = Scan::allScans.begin();
it != Scan::allScans.end();
++it)
{
while(DebugDrawFinished ==false && !veryQuiet)
{
Sleep(1);
}
VeloScan* currentScan =(VeloScan* ) *it;
currentScan->setRangeFilter(maxDist, minDist);
currentScan->setReductionParameter(red, octree);
currentScan->setSearchTreeParameter(nns_method, cuda_enabled);
currentScan->isTrackerHandled=false;
currentScan->scanid = scanCount; ///
ICPFinished =false;
if(tracking ==1 )
{
currentScan->FindingAllofObject(maxDist, minDist);
currentScan->ClassifiAllofObject();
}
if(tracking ==2 )
{
int windowsize =3;
currentScan->FindingAllofObject(maxDist, minDist);
currentScan->TrackingAllofObject(trackingAlgo);
currentScan->ClassifibyTrackingAllObject(scanCount, windowsize);
// trackMgr.ListTrackers();
}
if( tracking ==0 || tracking ==1 ||tracking ==2 )
{
currentScan->ExchangePointCloud();
currentScan->calcReducedPoints_byClassifi(red, octree, PointType());
}
currentScan->createSearchTree();
#ifdef NO_SLIDING_WINDOW
MatchTwoScan(my_icp, currentScan, scanCount, eP);
#else
if(current_sliding_window_pos > sliding_window_size )
MatchTwoScan(my_icp, currentScan, sliding_window_size, eP);
else
MatchTwoScan(my_icp, currentScan, scanCount, eP);
#endif
// update the cluster position in trakers.
///////////////////////////////////////////////////////////////////
const double* p;
p = currentScan->get_rPos();
Point x(p[0], p[1], p[2]);
VelodyneTrajectory.path.push_back(x);
//////////////////////////////////////////
scanCount++;
current_sliding_window_pos++;
#ifdef NO_SLIDING_WINDOW
#else
if(current_sliding_window_pos > sliding_window_size )
{
vector <Scan*>::iterator Iter = Scan::allScans.begin();
VeloScan *deleteScan = (VeloScan*)(*Iter);
// cout << "delete scan " << deleteScan->scanid << endl;
delete deleteScan;
}
#endif
ICPFinished =true;
if(!veryQuiet)
{
glutPostRedisplay();
}
while(DebugDrawFinished ==false && !veryQuiet)
{
Sleep(1);
}
////////////////////////////////////////
}
// long starttime = GetCurrentTimeInMilliSec();
// //Finall graph Matching
// FinalSLAM( red, rand,
// mdm, mdml, mdmll,
// mni, start, end, maxDist, minDist, quiet, veryQuiet,
// eP, meta, algo, loopSlam6DAlgo, lum6DAlgo, anim,
// mni_lum, net, cldist, clpairs, loopsize,
// epsilonICP, epsilonSLAM, nns_method, exportPts, distLoop,
// iterLoop, graphDist, octree, cuda_enabled, type
//);
if (exportPts)
{
cout << "Export all 3D Points to file \"points.pts\"" << endl;
ofstream redptsout("points.pts");
for(unsigned int i = 0; i < Scan::allScans.size(); i++)
{
DataXYZ xyz_r(Scan::allScans[i]->get("xyz reduced"));
for(unsigned int i = 0; i < xyz_r.size(); ++i)
redptsout << xyz_r[i][0] << ' ' << xyz_r[i][1] << ' ' << xyz_r[i][2] << '\n';
redptsout << std::flush;
}
}
for(ScanVector::iterator it = Scan::allScans.begin(); it != Scan::allScans.end(); ++it)
{
Scan* scan = *it;
scan->saveFrames();
}
Scan::closeDirectory();
delete my_icp6Dminimizer;
delete my_icp;
cout << endl << endl;
cout << "Normal program end." << endl;
}
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