Dense-depth map guided deep Lidar-Visual Odometry with Sparse Point Clouds and Images
This addresses odometry for autonomous navigation, but it appears incremental as it builds on existing LiDAR-visual fusion approaches.
The paper tackles the problem of accurate and robust pose estimation for autonomous systems by integrating LiDAR point clouds and images, achieving similar or superior accuracy and robustness compared to state-of-the-art methods on the KITTI odometry benchmark.
Odometry is a critical task for autonomous systems for self-localization and navigation. We propose a novel LiDAR-Visual odometry framework that integrates LiDAR point clouds and images for accurate and robust pose estimation. Our method utilizes a dense-depth map estimated from point clouds and images through depth completion, and incorporates a multi-scale feature extraction network with attention mechanisms, enabling adaptive depth-aware representations. Furthermore, we leverage dense depth information to refine flow estimation and mitigate errors in occlusion-prone regions. Our hierarchical pose refinement module optimizes motion estimation progressively, ensuring robust predictions against dynamic environments and scale ambiguities. Comprehensive experiments on the KITTI odometry benchmark demonstrate that our approach achieves similar or superior accuracy and robustness compared to state-of-the-art visual and LiDAR odometry methods.