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, we propose MSPA-LIO, a LiDAR-Inertial odometry with multi-scale plane adjustment, which uses geometric constraints and plane adjustment at both local voxel plane scale and large plane scale to improve odometry accuracy and enhance map consistency. In order to make full use of the planar structure in the environment, we propose an explicit large plane extraction method based on the voxel-based. We use large planes to correct the direction of the associated voxel planes, thereby overcoming the misregistration problem caused by local plane tilt. To further improve the odometry accuracy, we perform plane adjustments at the voxel plane scale and the large plane scale to make the pose and map more consistent. Experiments conducted on the VECtor Dataset and the Newer College Dataset demonstrate that our proposed algorithm outperforms four state-of-the-art algorithms.

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 develop VPE-SLAM, based on the proposed voxel-permutohedral encoding, which can incrementally reconstruct maps of unknown scenes. Specifically, voxel-permutohedral encoding combines a sparse voxel feature grid created by an octree and multi-resolution permutohedral tetrahedral feature grids to represent the scene effectively. Especially when dealing with object edges, our method can effectively encode the geometry and texture of edges by the hybrid structural grid. We propose a novel local bundle adjustment module that utilizes a sliding window mechanism to manage adjacent keyframes requiring optimization. Furthermore, the proposed method establishes local map consistency by repeatedly optimizing keyframes that were initially under-optimized through a compensation strategy. The consistency of the local map can enhance the adaptability of our method to challenging scenes. Extensive experiments demonstrate that our method can achieve accurate camera tracking and produce high-quality reconstruction results on the Replica and ScanNet datasets. The source code will be available at https://github.com/NeuCV-IRMI/VPE-SLAM.

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, which greatly limits their application in actual high-dynamic scenes. To address this issue, we propose ESO-SLAM, a tightly-coupled system for simultaneous self and multi-object pose estimation achieved through sensor fusion. This system employs a multi-probability fusion tracker based on filter to establish more robust object-level data association. Building upon this, we introduce a method that combines 3D Kalman filter velocity priors and camera optical flow decoupling for dynamic point cloud removal, aiming at improving the accuracy of self-pose estimation in odometry. Finally, we jointly refine the poses of the robot and objects using multiple constraint factors within our proposed framework. Experimental results on the KITTI raw dataset demonstrate that our approach achieves better pose accuracy for both self and tracked objects compared to baseline and state-of-the-art techniques. Furthermore, the proposed method exhibits feasibility in real-time performance to ensure its practical application value.

Place recognition is a critical technology in robot navigation and autonomous driving, remains challenging due to inefficient point cloud computation, limited feature representation capability, and poor robustness to long-term environmental changes. We propose MVSE-Net, a feature extraction network with embedded semantic information for multi-view feature fusion. MVSE-Net can convert point cloud data acquired by LiDAR in real time into global descriptors for retrieval. Processing a point cloud by projecting it onto a 2D image can greatly improve computational efficiency. We projected the point cloud into a range-view (RV) image and a bird’s-eye-view (BEV) image in forward and top view, respectively. The semantic segmentation network is then used to process the RV image, and the feature extraction part of the semantic model is connected to the transformer attention module to further refine the features for the place recognition task. The point cloud containing the semantic segmentation results is then converted into a semantic BEV image, and the multi-channel BEV image is processed using a group convolutional network. Finally, the features of the two branches are fused into a global feature representation by post-fusion. Our experiments on three publicly available datasets demonstrate that MVSE-Net exhibits high recall and strong generalization in LiDAR place recognition, outperforming previous state-of-the-art methods.