diff options
Diffstat (limited to 'tools/lighthousefind_radii/lighthousefind_radii.c')
| -rw-r--r-- | tools/lighthousefind_radii/lighthousefind_radii.c | 557 |
1 files changed, 557 insertions, 0 deletions
diff --git a/tools/lighthousefind_radii/lighthousefind_radii.c b/tools/lighthousefind_radii/lighthousefind_radii.c new file mode 100644 index 0000000..8a684cb --- /dev/null +++ b/tools/lighthousefind_radii/lighthousefind_radii.c @@ -0,0 +1,557 @@ +#include <stdio.h> +#include <stdlib.h> +#include "linmath.h" +#include <string.h> +#include <stdint.h> +#include <math.h> + +#define PTS 32 +#define MAX_CHECKS 40000 +#define MIN_HITS_FOR_VALID 10 + +FLT hmd_points[PTS*3]; +FLT hmd_norms[PTS*3]; +FLT hmd_point_angles[PTS*2]; +int hmd_point_counts[PTS*2]; +int best_hmd_target = 0; +int LoadData( char Camera, const char * FileData ); + +//Values used for RunTest() +FLT LighthousePos[3] = { 0, 0, 0 }; +FLT LighthouseQuat[4] = { 1, 0, 0, 0 }; + +FLT RunTest( int print ); +void PrintOpti(); + +#define MAX_POINT_PAIRS 100 + +typedef struct +{ + FLT x; + FLT y; + FLT z; +} Point; + +typedef struct +{ + Point point; // location of the sensor on the tracked object; + Point normal; // unit vector indicating the normal for the sensor + double theta; // "horizontal" angular measurement from lighthouse radians + double phi; // "vertical" angular measurement from lighthouse in radians. +} TrackedSensor; + +typedef struct +{ + size_t numSensors; + TrackedSensor sensor[0]; +} TrackedObject; + +typedef struct +{ + unsigned char index1; + unsigned char index2; + FLT KnownDistance; +} PointPair; + +static FLT distance(Point a, Point b) +{ + FLT x = a.x - b.x; + FLT y = a.y - b.y; + FLT z = a.z - b.z; + return FLT_SQRT(x*x + y*y + z*z); +} + +typedef struct +{ + FLT HorizAngle; + FLT VertAngle; +} SensorAngles; + +#define SQUARED(x) ((x)*(x)) + +FLT calculateFitnessOld(SensorAngles *angles, FLT *radii, PointPair *pairs, size_t numPairs) +{ + FLT fitness = 0; + for (size_t i = 0; i < numPairs; i++) + { + FLT estimatedDistanceBetweenPoints = + SQUARED(radii[pairs[i].index1]) + + SQUARED(radii[pairs[i].index2]) + - 2 * radii[pairs[i].index1] * radii[pairs[i].index2] + * FLT_SIN(angles[pairs[i].index1].HorizAngle) * FLT_SIN(angles[pairs[i].index2].HorizAngle) + * FLT_COS(angles[pairs[i].index1].VertAngle - angles[pairs[i].index2].VertAngle) + + FLT_COS(angles[pairs[i].index1].VertAngle) * FLT_COS(angles[pairs[i].index2].VertAngle); + fitness += SQUARED(estimatedDistanceBetweenPoints - pairs[i].KnownDistance); + } + + return FLT_SQRT(fitness); +} + +FLT calculateFitness(SensorAngles *angles, FLT *radii, PointPair *pairs, size_t numPairs) +{ + FLT fitness = 0; + for (size_t i = 0; i < numPairs; i++) + { + // These are the vectors that represent the direction for the two points. + // TODO: optimize by precomputing the tangent. + FLT v1[3], v2[3], diff[3]; + + v1[0] = 1; + v2[0] = 1; + v1[1] = tan(angles[pairs[i].index1].HorizAngle); // can be precomputed + v2[1] = tan(angles[pairs[i].index2].HorizAngle); // can be precomputed + v1[2] = tan(angles[pairs[i].index1].VertAngle); // can be precomputed + v2[2] = tan(angles[pairs[i].index2].VertAngle); // can be precomputed + + // Now, normalize the vectors + normalize3d(v1, v1); // can be precomputed + normalize3d(v2, v2); // can be precomputed + + // Now, given the specified radii, find where the new points are + scale3d(v1, v1, radii[pairs[i].index1]); + scale3d(v2, v2, radii[pairs[i].index2]); + + // Cool, now find the vector between these two points + // TODO: optimize the following two funcs into one. + sub3d(diff, v1, v2); + + FLT distance = magnitude3d(diff); + + FLT t1 = magnitude3d(v1); + FLT t2 = magnitude3d(v2); + + + + FLT estimatedDistanceBetweenPoints = + + SQUARED(radii[pairs[i].index1]) + + SQUARED(radii[pairs[i].index2]) + - 2 * radii[pairs[i].index1] * radii[pairs[i].index2] + * FLT_SIN(angles[pairs[i].index1].HorizAngle) * FLT_SIN(angles[pairs[i].index2].HorizAngle) + * FLT_COS(angles[pairs[i].index1].VertAngle - angles[pairs[i].index2].VertAngle) + + FLT_COS(angles[pairs[i].index1].VertAngle) * FLT_COS(angles[pairs[i].index2].VertAngle); + + + //fitness += SQUARED(estimatedDistanceBetweenPoints - pairs[i].KnownDistance); + fitness += SQUARED(distance - pairs[i].KnownDistance); + } + + return FLT_SQRT(fitness); +} + +#define MAX_RADII 32 + +// note gradientOut will be of the same degree as numRadii +void getGradient(FLT *gradientOut, SensorAngles *angles, FLT *radii, size_t numRadii, PointPair *pairs, size_t numPairs, const FLT precision) +{ + FLT baseline = calculateFitness(angles, radii, pairs, numPairs); + + for (size_t i = 0; i < numRadii; i++) + { + FLT tmpPlus[MAX_RADII]; + memcpy(tmpPlus, radii, sizeof(*radii) * numRadii); + tmpPlus[i] += precision; + gradientOut[i] = -(calculateFitness(angles, tmpPlus, pairs, numPairs) - baseline); + } + + return; +} + +void normalizeAndMultiplyVector(FLT *vectorToNormalize, size_t count, FLT desiredMagnitude) +{ + FLT distanceIn = 0; + + for (size_t i = 0; i < count; i++) + { + distanceIn += SQUARED(vectorToNormalize[i]); + } + distanceIn = FLT_SQRT(distanceIn); + + + FLT scale = desiredMagnitude / distanceIn; + + for (size_t i = 0; i < count; i++) + { + vectorToNormalize[i] *= scale; + } + + return; +} + + +static RefineEstimateUsingGradientDescent(FLT *estimateOut, SensorAngles *angles, FLT *initialEstimate, size_t numRadii, PointPair *pairs, size_t numPairs, FILE *logFile) +{ + int i = 0; + FLT lastMatchFitness = calculateFitness(angles, initialEstimate, pairs, numPairs); + if (estimateOut != initialEstimate) + { + memcpy(estimateOut, initialEstimate, sizeof(*estimateOut) * numRadii); + } + + + // The values below are somewhat magic, and definitely tunable + // The initial vlue of g will represent the biggest step that the gradient descent can take at first. + // bigger values may be faster, especially when the initial guess is wildly off. + // The downside to a bigger starting guess is that if we've picked a good guess at the local minima + // if there are other local minima, we may accidentally jump to such a local minima and get stuck there. + // That's fairly unlikely with the lighthouse problem, from expereince. + // The other downside is that if it's too big, we may have to spend a few iterations before it gets down + // to a size that doesn't jump us out of our minima. + // The terminal value of g represents how close we want to get to the local minima before we're "done" + // The change in value of g for each iteration is intentionally very close to 1. + // in fact, it probably could probably be 1 without any issue. The main place where g is decremented + // is in the block below when we've made a jump that results in a worse fitness than we're starting at. + // In those cases, we don't take the jump, and instead lower the value of g and try again. + for (FLT g = 0.4; g > 0.00001; g *= 0.9999) + { + i++; + + + + FLT point1[MAX_RADII]; + memcpy(point1, estimateOut, sizeof(*point1) * numRadii); + + // let's get 3 iterations of gradient descent here. + FLT gradient1[MAX_RADII]; + getGradient(gradient1, angles, point1, numRadii, pairs, numPairs, g / 1000 /*somewhat arbitrary*/); + normalizeAndMultiplyVector(gradient1, numRadii, g); + + FLT point2[MAX_RADII]; + for (size_t i = 0; i < numRadii; i++) + { + point2[i] = point1[i] + gradient1[i]; + } + FLT gradient2[MAX_RADII]; + getGradient(gradient2, angles, point2, numRadii, pairs, numPairs, g / 1000 /*somewhat arbitrary*/); + normalizeAndMultiplyVector(gradient2, numRadii, g); + + FLT point3[MAX_RADII]; + for (size_t i = 0; i < numRadii; i++) + { + point3[i] = point2[i] + gradient2[i]; + } + + // remember that gradient descent has a tendency to zig-zag when it encounters a narrow valley? + // Well, solving the lighthouse problem presents a very narrow valley, and the zig-zag of a basic + // gradient descent is kinda horrible here. Instead, think about the shape that a zig-zagging + // converging gradient descent makes. Instead of using the gradient as the best indicator of + // the direction we should follow, we're looking at one side of the zig-zag pattern, and specifically + // following *that* vector. As it turns out, this works *amazingly* well. + + FLT specialGradient[MAX_RADII]; + for (size_t i = 0; i < numRadii; i++) + { + specialGradient[i] = point3[i] - point1[i]; + } + + // The second parameter to this function is very much a tunable parameter. Different values will result + // in a different number of iterations before we get to the minimum. Numbers between 3-10 seem to work well + // It's not clear what would be optimum here. + normalizeAndMultiplyVector(specialGradient, numRadii, g/4); + + + FLT point4[MAX_RADII]; + for (size_t i = 0; i < numRadii; i++) + { + point4[i] = point3[i] + specialGradient[i]; + } + + + FLT newMatchFitness = calculateFitness(angles, point4, pairs, numPairs); + + + if (newMatchFitness < lastMatchFitness) + { + //if (logFile) + //{ + // writePoint(logFile, lastPoint.x, lastPoint.y, lastPoint.z, 0xFFFFFF); + //} + + lastMatchFitness = newMatchFitness; + memcpy(estimateOut, point4, sizeof(*estimateOut) * numRadii); + +#ifdef RADII_DEBUG + printf("+ %d %0.9f (%0.9f) ", i, newMatchFitness, g); +#endif + g = g * 1.05; + } + else + { +#ifdef RADII_DEBUG +// printf("-"); + printf("- %d %0.9f (%0.9f) ", i, newMatchFitness, g); +#endif + // if it wasn't a match, back off on the distance we jump + g *= 0.7; + + } + +#ifdef RADII_DEBUG + FLT avg=0; + FLT diffFromAvg[MAX_RADII]; + + for (size_t m = 0; m < numRadii; m++) + { + avg += estimateOut[m]; + } + avg = avg / numRadii; + + for (size_t m = 0; m < numRadii; m++) + { + diffFromAvg[m] = estimateOut[m] - avg;; + } + printf("[avg:%f] ", avg); + + for (size_t x = 0; x < numRadii; x++) + { + printf("%f, ", diffFromAvg[x]); + //printf("%f, ", estimateOut[x]); + } + printf("\n"); + + +#endif + + + } + printf("\ni=%d\n", i); +} + +void SolveForLighthouse(Point *objPosition, FLT *objOrientation, TrackedObject *obj) +{ + FLT estimate[MAX_RADII]; + + for (size_t i = 0; i < MAX_RADII; i++) + { + estimate[i] = 2.4; + } + + SensorAngles angles[MAX_RADII]; + PointPair pairs[MAX_POINT_PAIRS]; + + size_t pairCount = 0; + + obj->numSensors = 7; // TODO: HACK!!!! + + for (size_t i = 0; i < obj->numSensors; i++) + { + angles[i].HorizAngle = obj->sensor[i].theta; + angles[i].VertAngle = obj->sensor[i].phi; + } + + for (size_t i = 0; i < obj->numSensors - 1; i++) + { + for (size_t j = i + 1; j < obj->numSensors; j++) + { + pairs[pairCount].index1 = i; + pairs[pairCount].index2 = j; + pairs[pairCount].KnownDistance = distance(obj->sensor[i].point, obj->sensor[j].point); + pairCount++; + } + } + + + RefineEstimateUsingGradientDescent(estimate, angles, estimate, obj->numSensors, pairs, pairCount, NULL); + + // we should now have an estimate of the radii. + + for (size_t i = 0; i < obj->numSensors; i++) + { + printf("radius[%d]: %f\n", i, estimate[i]); + } +// (FLT *estimateOut, SensorAngles *angles, FLT *initialEstimate, size_t numRadii, PointPair *pairs, size_t numPairs, FILE *logFile) + + getc(stdin); + return; +} + +static void runTheNumbers() +{ + TrackedObject *to; + + to = malloc(sizeof(TrackedObject) + (PTS * sizeof(TrackedSensor))); + + int sensorCount = 0; + + for (int i = 0; i < PTS; i++) + { + // if there are enough valid counts for both the x and y sweeps for sensor i + if ((hmd_point_counts[2 * i] > MIN_HITS_FOR_VALID) && + (hmd_point_counts[2 * i + 1] > MIN_HITS_FOR_VALID)) + { + to->sensor[sensorCount].point.x = hmd_points[i * 3 + 0]; + to->sensor[sensorCount].point.y = hmd_points[i * 3 + 1]; + to->sensor[sensorCount].point.z = hmd_points[i * 3 + 2]; + to->sensor[sensorCount].normal.x = hmd_norms[i * 3 + 0]; + to->sensor[sensorCount].normal.y = hmd_norms[i * 3 + 1]; + to->sensor[sensorCount].normal.z = hmd_norms[i * 3 + 2]; + to->sensor[sensorCount].theta = hmd_point_angles[i * 2 + 0] + LINMATHPI / 2; + to->sensor[sensorCount].phi = hmd_point_angles[i * 2 + 1] + LINMATHPI / 2; + sensorCount++; + } + } + + to->numSensors = sensorCount; + + printf("Using %d sensors to find lighthouse.\n", sensorCount); + + Point lh; + for (int i = 0; i < 1; i++) + { + SolveForLighthouse(&lh, NULL, to); + //(0.156754, -2.403268, 2.280167) + //assert(fabs((lh.x / 0.1419305302702402) - 1) < 0.00001); + //assert(fabs((lh.y / 2.5574949720325431) - 1) < 0.00001); + //assert(fabs((lh.z / 2.2451193935772080) - 1) < 0.00001); + //assert(lh.x > 0); + //assert(lh.y > 0); + //assert(lh.z > 0); + } + + printf("(%f, %f, %f)\n", lh.x, lh.y, lh.z); + + //printTrackedObject(to); + free(to); +} + +int main( int argc, char ** argv ) +{ + + if( argc != 3 ) + { + fprintf( stderr, "Error: usage: camfind [camera (L or R)] [datafile]\n" ); + exit( -1 ); + } + + //Load either 'L' (LH1) or 'R' (LH2) data. + if( LoadData( argv[1][0], argv[2] ) ) return 5; + + runTheNumbers(); + + + +} + + + + + +int LoadData( char Camera, const char * datafile ) +{ + + //First, read the positions of all the sensors on the HMD. + FILE * f = fopen( "HMD_points.csv", "r" ); + int pt = 0; + if( !f ) { fprintf( stderr, "error: can't open hmd points.\n" ); return -5; } + while(!feof(f) && !ferror(f) && pt < PTS) + { + float fa, fb, fc; + int r = fscanf( f,"%g %g %g\n", &fa, &fb, &fc ); + hmd_points[pt*3+0] = fa; + hmd_points[pt*3+1] = fb; + hmd_points[pt*3+2] = fc; + pt++; + if( r != 3 ) + { + fprintf( stderr, "Not enough entries on line %d of points\n", pt ); + return -8; + } + } + if( pt < PTS ) + { + fprintf( stderr, "Not enough points.\n" ); + return -9; + } + fclose( f ); + printf( "Loaded %d points\n", pt ); + + //Read all the normals on the HMD into hmd_norms. + f = fopen( "HMD_normals.csv", "r" ); + int nrm = 0; + if( !f ) { fprintf( stderr, "error: can't open hmd points.\n" ); return -5; } + while(!feof(f) && !ferror(f) && nrm < PTS) + { + float fa, fb, fc; + int r = fscanf( f,"%g %g %g\n", &fa, &fb, &fc ); + hmd_norms[nrm*3+0] = fa; + hmd_norms[nrm*3+1] = fb; + hmd_norms[nrm*3+2] = fc; + nrm++; + if( r != 3 ) + { + fprintf( stderr, "Not enough entries on line %d of normals\n", nrm ); + return -8; + } + } + if( nrm < PTS ) + { + fprintf( stderr, "Not enough points.\n" ); + return -9; + } + if( nrm != pt ) + { + fprintf( stderr, "point/normal counts disagree.\n" ); + return -9; + } + fclose( f ); + printf( "Loaded %d norms\n", nrm ); + + //Actually load the processed data! + int xck = 0; + f = fopen( datafile, "r" ); + if( !f ) + { + fprintf( stderr, "Error: cannot open %s\n", datafile ); + exit (-11); + } + int lineno = 0; + while( !feof( f ) ) + { + //Format: + // HMD LX 0 3433 173656.227498 327.160210 36.342361 2.990936 + lineno++; + char devn[10]; + char inn[10]; + int id; + int pointct; + FLT avgTime; + FLT avgLen; + FLT stddevTime; + FLT stddevLen; + int ct = fscanf( f, "%9s %9s %d %d %lf %lf %lf %lf\n", devn, inn, &id, &pointct, &avgTime, &avgLen, &stddevTime, &stddevLen ); + if( ct == 0 ) continue; + if( ct != 8 ) + { + fprintf( stderr, "Malformatted line, %d in processed_data.txt\n", lineno ); + } + if( strcmp( devn, "HMD" ) != 0 ) continue; + + if( inn[0] != Camera ) continue; + + int isy = inn[1] == 'Y'; + + hmd_point_angles[id*2+isy] = ( avgTime - 200000 ) / 200000 * 3.1415926535/2.0; + hmd_point_counts[id*2+isy] = pointct; + } + fclose( f ); + + + int targpd; + int maxhits = 0; + + for( targpd = 0; targpd < PTS; targpd++ ) + { + int hits = hmd_point_counts[targpd*2+0]; + if( hits > hmd_point_counts[targpd*2+1] ) hits = hmd_point_counts[targpd*2+1]; + //Need an X and a Y lock. + + if( hits > maxhits ) { maxhits = hits; best_hmd_target = targpd; } + } + if( maxhits < MIN_HITS_FOR_VALID ) + { + fprintf( stderr, "Error: Not enough data for a primary fix.\n" ); + } + + return 0; +} + |