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Implementation of the LOST triangulation algorithm #1241
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Its possible my design choice may not be the best. The goal was to have the LOST implementation share the same triangulatePoint3 interface. |
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Awesome! some naming nits - we use camelCase, but after that merge at will.
@@ -96,6 +96,70 @@ TEST(triangulation, twoPoses) { | |||
EXPECT(assert_equal(Point3(4.995, 0.499167, 1.19814), *actual4, 1e-4)); | |||
} | |||
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TEST(triangulation, twoCamerasUsingLOST) { | |||
CameraSet<PinholeCamera<Cal3_S2>> cameras; | |||
cameras.push_back(camera1); |
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Let’s rename global constants in this file kCamera1, kCamera2 etc and make sure they are static const.
// LOST has been shown to preform better when the point is much closer to one | ||
// camera compared to another. This unit test checks this configuration. | ||
Cal3_S2 identity_K; | ||
Pose3 pose_1; |
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pose1, pose2, identityK
gtsam/geometry/triangulation.h
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@@ -302,8 +316,8 @@ template <class CAMERA> | |||
typename CAMERA::MeasurementVector undistortMeasurements( | |||
const CameraSet<CAMERA>& cameras, | |||
const typename CAMERA::MeasurementVector& measurements) { | |||
const size_t num_meas = cameras.size(); | |||
assert(num_meas == measurements.size()); | |||
const size_t num_meas = measurements.size(); |
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rename to nrMeasurements
and nrCameras
gtsam/geometry/triangulation.h
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const Point2Vector& measurements, | ||
double rank_tol = 1e-9, bool optimize = false, | ||
const SharedNoiseModel& model = nullptr, | ||
const bool use_lost_triangulation = false) { |
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In GTSAM we use camelCase. useLOST
seems good.
PrintDuration(lost_duration, num_trials, "LOST"); | ||
PrintDuration(dlt_duration, num_trials, "DLT"); | ||
PrintDuration(dlt_opt_duration, num_trials, "DLT_OPT"); | ||
} |
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Add newline
In collaboration with Sebastien Henry and Prof. John Christian, we implement the LOST triangulation algorithm https://arxiv.org/pdf/2205.12197.pdf.
The triangulatePoint3 method accepts a boolean that initializes the point using the LOST method instead of DLT.
This PR also adds an example that runs DLT, LOST and DLT+nonlinear optimization. It shows that the covariance of LOST is similar to that of DLT + optimization, while being much faster.