CT-VoxelMap achieves more accurate and efficient continuous-time LiDAR-inertial odometry by estimating control point increments on Lie groups, using IMU data to correct B-spline fitting errors online, and managing a probabilistic adaptive voxel map with a re-estimation policy.
Sparse Gaussian Processes for Continuous-Time Trajectory Estimation on Matrix Lie Groups
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abstract
Continuous-time trajectory representations are a powerful tool that can be used to address several issues in many practical simultaneous localization and mapping (SLAM) scenarios, like continuously collected measurements distorted by robot motion, or during with asynchronous sensor measurements. Sparse Gaussian processes (GP) allow for a probabilistic non-parametric trajectory representation that enables fast trajectory estimation by sparse GP regression. However, previous approaches are limited to dealing with vector space representations of state only. In this technical report we extend the work by Barfoot et al. [1] to general matrix Lie groups, by applying constant-velocity prior, and defining locally linear GP. This enables using sparse GP approach in a large space of practical SLAM settings. In this report we give the theory and leave the experimental evaluation in future publications.
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A motion planning algorithm using cross-entropy stochastic optimization on heteroscedastic Gaussian process trajectories reports higher success rates than GPMP2 in complex environments with comparable runtime.
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CT-VoxelMap: Efficient Continuous-Time LiDAR-Inertial Odometry with Probabilistic Adaptive Voxel Mapping
CT-VoxelMap achieves more accurate and efficient continuous-time LiDAR-inertial odometry by estimating control point increments on Lie groups, using IMU data to correct B-spline fitting errors online, and managing a probabilistic adaptive voxel map with a re-estimation policy.
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Stochastic Optimization for Trajectory Planning with Heteroscedastic Gaussian Processes
A motion planning algorithm using cross-entropy stochastic optimization on heteroscedastic Gaussian process trajectories reports higher success rates than GPMP2 in complex environments with comparable runtime.