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Fixing crash on windows when uFormat() is to long

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matlabbe
2026-10-11 11:45:52 -07:00
parent 526c422643
commit 05b5661e6b
6 changed files with 37 additions and 4 deletions
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corelib/include/rtabmap/core/Parameters.h
+2 -2
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@@ -489,7 +489,7 @@ class RTABMAP_CORE_EXPORT Parameters
RTABMAP_PARAM(Optimizer, Baseline, double, 0.075, "When doing bundle adjustment with RGB-D data (mono camera + depth), set a fake baseline (m) so the BA backend treats depth as stereo disparity. Applies to all BA-capable backends (g2o, GTSAM, Ceres). Set to 0 to keep the problem mono (depth observations are ignored). For real stereo data the baseline in the calibration (Tx) is used directly.");
RTABMAP_PARAM(Optimizer, PixelVariance, double, 1.0, "Pixel variance used on the u/v axes of every bundle adjustment reprojection edge. Applies to all BA-capable backends (g2o, GTSAM, Ceres). Should approximate the squared 1-sigma keypoint localization error in pixels. Set higher (e.g. 4-9) if features are noisy (low texture, motion blur, low light, or large detector scale). Set lower (e.g. 0.01-0.1) if features are sub-pixel refined (Lucas-Kanade tracking, parabolic peak interpolation). Intuition: the lower the pixel variance, the more the optimizer trusts the keypoint positions.");
RTABMAP_PARAM(Optimizer, DisparityVariance, double, 1.0, "Disparity variance used on the disparity axis (u - u_right) of stereo / RGB-D bundle adjustment edges. Applies to all BA-capable backends (g2o, GTSAM, Ceres). Defaults to the same value as PixelVariance for backward compatibility. Set higher (e.g. 2-4) if your depth source is noisier than your feature detector's u/v precision (typical for stereo block matchers / SGM at long range). Set lower (e.g. 0.01-0.1) if your depth source is more accurate than the u/v detector (typical for ToF / LiDAR-fused depth where range is measured directly rather than triangulated). Intuition: the lower the disparity variance, the more the optimizer trusts the depth measurements. Geometric note: wider baseline and/or higher image resolution improve a block matcher's effective disparity precision (larger disparity magnitudes and finer sub-pixel refinement), so wide-baseline high-resolution stereo pairs can usually afford a lower disparity variance (e.g. 0.1-0.5); narrow-baseline low-resolution pairs should keep it higher (e.g. 1-4).");
RTABMAP_PARAM(Optimizer, DisparityVariance, double, 1.0, "Disparity variance used on the disparity axis (u - u_right) of stereo / RGB-D bundle adjustment edges (g2o, GTSAM, Ceres). Defaults to the same value as PixelVariance for backward compatibility. The lower it is, the more the optimizer trusts the depth. Set higher (e.g. 2-4) if the depth is noisier than the features' u/v precision (stereo block matchers / SGM at long range, narrow-baseline low-resolution stereo). Set lower (e.g. 0.01-0.1) if it is more accurate (ToF / LiDAR-fused depth, measured rather than triangulated), or around 0.1-0.5 for wide-baseline high-resolution stereo.");
RTABMAP_PARAM(Optimizer, RobustKernelDelta, double, 8, "Robust kernel delta used for bundle adjustment (0 means don't use robust kernel). Applies to all BA-capable backends (g2o, GTSAM, Ceres). Observations with chi2 over this threshold will be ignored in the second optimization pass.");
RTABMAP_PARAM(GTSAM, Optimizer, int, 1, "0=Levenberg 1=GaussNewton 2=Dogleg");
@@ -513,7 +513,7 @@ class RTABMAP_CORE_EXPORT Parameters
RTABMAP_PARAM(Odom, KalmanMeasurementNoise, float, 0.01, "Process measurement covariance value.");
RTABMAP_PARAM(Odom, GuessMotion, bool, true, "Guess next transformation from the last motion computed.");
RTABMAP_PARAM(Odom, ImuGravity, float, 9.80665, uFormat("Gravity magnitude (m/s^2) removed from the IMU linear acceleration (used with \"%s\" > 0). Standard gravity by default. Set it to what the accelerometer reads at rest to compensate for its scale error, or for another gravity than Earth's. 0: estimated from the IMU while it is still (gyro and accelerometer quiet over 0.5 s), with standard gravity until then: the robot should then be perfectly still for a moment (e.g., at start), and a steady acceleration without rotation would be taken for gravity.", kOdomGuessSmoothingDelay().c_str()));
RTABMAP_PARAM(Odom, GuessSmoothingDelay, float, 0, uFormat("Guess smoothing delay (s). Estimated velocity is averaged based on last transforms up to this maximum delay. This can help to get smoother velocity prediction. Last velocity computed is used directly if \"%s\" is set or the delay is below the odometry rate. With an IMU giving orientation and linear acceleration, a delay > 0 also enables the IMU acceleration: the velocity is then the displacement over this delay corrected by the acceleration measured since, so that it is the velocity at the last frame rather than a delayed average, and the motion guess and lidar deskewing (\"%s\") integrate the acceleration from it. Recommended (~0.5 s) with an IMU. With 0, the IMU only gives the orientation. Without IMU, the average is delayed by half this delay: it can make sense for platforms with inertia (e.g., cars at high speed, where one bad frame would otherwise change the predicted velocity a lot), for high frame rates, or when the velocity is used for lidar deskewing, where its noise would feed back into the next poses.", kOdomFilteringStrategy().c_str(), kOdomDeskewing().c_str()));
RTABMAP_PARAM(Odom, GuessSmoothingDelay, float, 0, uFormat("Guess smoothing delay (s). The velocity is averaged over the last transforms up to this delay, for a smoother velocity prediction. The last velocity is used directly if \"%s\" is set or the delay is below the odometry rate. With an IMU giving orientation and linear acceleration, a delay > 0 also enables the IMU acceleration: the velocity is the displacement over this delay corrected by the acceleration measured since, so it is not delayed, and the motion guess and lidar deskewing (\"%s\") integrate the acceleration from it (~0.5 s recommended). With 0, the IMU only gives the orientation. Without IMU, the average is delayed by half this delay: useful for platforms with inertia, high frame rates, or lidar deskewing, where the velocity noise would feed back into the next poses.", kOdomFilteringStrategy().c_str(), kOdomDeskewing().c_str()));
RTABMAP_PARAM(Odom, KeyFrameThr, float, 0.3, "[Visual] Create a new keyframe when the number of inliers drops under this ratio of features in last frame. Setting the value to 0 means that a keyframe is created for each processed frame.");
RTABMAP_PARAM(Odom, VisKeyFrameThr, int, 150, "[Visual] Create a new keyframe when the number of inliers drops under this threshold. Setting the value to 0 means that a keyframe is created for each processed frame.");
RTABMAP_PARAM(Odom, ScanKeyFrameThr, float, 0.9, "[Geometry] Create a new keyframe when the number of ICP inliers drops under this ratio of points in last frame's scan. Setting the value to 0 means that a keyframe is created for each processed frame.");