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author | mwturvey <michael.w.turvey@intel.com> | 2017-03-10 12:44:10 -0700 |
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committer | mwturvey <michael.w.turvey@intel.com> | 2017-03-10 12:44:10 -0700 |

commit | 1d9db12d7e115f2b8994f014e37f1086c17e90fd (patch) | |

tree | 5f88cb913bd15130995a4bba94f6806509d15642 /tools | |

parent | 8136850901bd817fdb23842e10febace47e5b18a (diff) | |

download | libsurvive-1d9db12d7e115f2b8994f014e37f1086c17e90fd.tar.gz libsurvive-1d9db12d7e115f2b8994f014e37f1086c17e90fd.tar.bz2 |

Cleanup & torus updates

Diffstat (limited to 'tools')

-rw-r--r-- | tools/lighthousefind_tori/torus_localizer.c | 141 |

1 files changed, 136 insertions, 5 deletions

diff --git a/tools/lighthousefind_tori/torus_localizer.c b/tools/lighthousefind_tori/torus_localizer.c index fd74b22..894bbce 100644 --- a/tools/lighthousefind_tori/torus_localizer.c +++ b/tools/lighthousefind_tori/torus_localizer.c @@ -7,12 +7,12 @@ #include "visualization.h" -static double distance(Point a, Point b) +static FLT distance(Point a, Point b) { - double x = a.x - b.x; - double y = a.y - b.y; - double z = a.z - b.z; - return sqrt(x*x + y*y + z*z); + 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); } Matrix3x3 GetRotationMatrixForTorus(Point a, Point b) @@ -457,6 +457,137 @@ static Point RefineEstimateUsingModifiedGradientDescent1(Point initialEstimate, } +// interesting-- this is one place where we could use any sensors that are only hit by +// just an x or y axis to make our estimate better. TODO: bring that data to this fn. +FLT RotationEstimateFitness(Point lhPoint, FLT *quaternion, TrackedObject *obj) +{ + for (size_t i = 0; i < obj->numSensors; i++) + { + // first, get the normal of the plane for the horizonal sweep + FLT theta = obj->sensor[i].theta; + // make two vectors that lie on the plane + FLT t1[3] = { 1, tan(theta), 0 }; + FLT t2[3] = { 1, tan(theta), 1 }; + + FLT tNorm[3]; + + // the normal is the cross of two vectors on the plane. + cross3d(tNorm, t1, t2); + + // distance for this plane is d= fabs(A*x + B*y)/sqrt(A^2+B^2) (z term goes away since this plane is "vertical") + // where A is + //FLT d = + } +} + +static Point RefineRotationEstimate(Point initialEstimate, PointsAndAngle *pna, size_t pnaCount, FILE *logFile) +{ + int i = 0; + FLT lastMatchFitness = getPointFitness(initialEstimate, pna, pnaCount); + Point lastPoint = initialEstimate; + + // 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 = 0.2; g > 0.00001; g *= 0.99) + { + i++; + Point point1 = lastPoint; + // let's get 3 iterations of gradient descent here. + Point gradient1 = getGradient(point1, pna, pnaCount, g / 1000 /*somewhat arbitrary*/); + Point gradientN1 = getNormalizedVector(gradient1, g); + + Point point2; + point2.x = point1.x + gradientN1.x; + point2.y = point1.y + gradientN1.y; + point2.z = point1.z + gradientN1.z; + + Point gradient2 = getGradient(point2, pna, pnaCount, g / 1000 /*somewhat arbitrary*/); + Point gradientN2 = getNormalizedVector(gradient2, g); + + Point point3; + point3.x = point2.x + gradientN2.x; + point3.y = point2.y + gradientN2.y; + point3.z = point2.z + gradientN2.z; + + // 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. + + Point specialGradient = { .x = point3.x - point1.x, .y = point3.y - point1.y, .z = point3.y - point1.y }; + + // 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. + specialGradient = getNormalizedVector(specialGradient, g / 4); + + Point point4; + + point4.x = point3.x + specialGradient.x; + point4.y = point3.y + specialGradient.y; + point4.z = point3.z + specialGradient.z; + + FLT newMatchFitness = getPointFitness(point4, pna, pnaCount); + + if (newMatchFitness > lastMatchFitness) + { + if (logFile) + { + writePoint(logFile, lastPoint.x, lastPoint.y, lastPoint.z, 0xFFFFFF); + } + + lastMatchFitness = newMatchFitness; + lastPoint = point4; +#ifdef TORI_DEBUG + printf("+"); +#endif + } + else + { +#ifdef TORI_DEBUG + printf("-"); +#endif + g *= 0.7; + + } + + + } + printf("\ni=%d\n", i); + + return lastPoint; +} + +void SolveForRotation(FLT rotOut[4], TrackedObject *obj, Point lh) +{ + + // Step 1, create initial quaternion for guess. + // This should have the lighthouse directly facing the tracked object. + Point trackedObjRelativeToLh = { .x = -lh.x, .y = -lh.y, .z = -lh.z }; + FLT theta = atan2(-lh.x, -lh.y); + FLT zAxis[3] = { 0, 0, 1 }; + FLT quat1[4]; + quatfromaxisangle(quat1, zAxis, theta); + // not correcting for phi, but that's less important. + + // Step 2, optimize the quaternion to match the data. + +} + + Point SolveForLighthouse(TrackedObject *obj, char doLogOutput) { PointsAndAngle pna[MAX_POINT_PAIRS]; |