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.
M2ud: A multi-model, multi-scenario, uneven-terrain dataset for ground robot with localization and mapping evaluation
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
dataset 1
citation-polarity summary
fields
cs.RO 1years
2026 1verdicts
UNVERDICTED 1roles
dataset 1polarities
use dataset 1representative citing papers
citing papers explorer
-
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.