5 accepted papers
LiDAR-based odometry is widely used in ground robot localization. However, current methods encounter challenges in accuracy and robustness due to structural degradation, system observational error, and accumulated error. To address the above issues, we propose CPBA-LIWO, a continuous-time LiDAR-Iner…
Adaptive Compressive Sensing (ACS) has attracted increasing attention for its ability to progressively improve image reconstruction quality by dynamically adjusting sampling allocation. Multi-stage sampling is a promising strategy that leverages intermediate reconstructions to guide sampling without…
Most current LiDAR-based odometry methods use point-to-local plane registration to constrain poses, ignoring the explicit plane structure in the environment. Due to noise interference and uneven distribution of point cloud, local planes are prone to tilt, resulting in registration errors. Therefore,
Simultaneous Localization and Mapping (SLAM) is widely used in applications such as robotics and autonomous driving, with methods involving multi-sensor fusion demonstrating excellent performance. However, they simply reject dynamic features and ignore the mutual benefits of self and dynamic objects…
NeRF can reconstruct incredibly realistic environmental maps in dense simultaneous localization and mapping, providing robots with more comprehensive scene map information. However, NeRF often struggles with geometric distortions in indoor reconstructions. To correct geometric distortions, we develo…