This paper presents FAST-LIVO2, a fast and direct LiDAR-inertial-visual odometry framework designed for accurate and robust state estimation in SLAM tasks, enabling real-time robotic applications. FAST-LIVO2 integrates IMU, LiDAR, and image data through an efficient error-state iterated Kalman filter (ESIKF). To address the dimensional mismatch between LiDAR and image measurements, we adopt a sequential update strategy. Efficiency is further enhanced using direct methods for LiDAR and visual data fusion: the LiDAR module registers raw points without extracting features, while the visual module minimizes photometric errors without relying on feature extraction. Both LiDAR and visual measurements are fused into a unified voxel map. The LiDAR module constructs the geometric structure, while the visual module links image patches to LiDAR points, enabling precise image alignment. Plane priors from LiDAR points improve alignment accuracy and are refined dynamically during the process. Additionally, an on-demand raycast operation and real-time image exposure estimation enhance robustness. Extensive experiments on benchmark and custom datasets demonstrate that FAST-LIVO2 outperforms state-of-the-art systems in accuracy, robustness, and efficiency. Key modules are validated, and we showcase three applications: UAV navigation highlighting real-time capabilities, airborne mapping demonstrating high accuracy, and 3D model rendering (mesh-based and NeRF-based) showcasing suitability for dense mapping. Code and datasets are open-sourced on GitHub to benefit the robotics community.

In this work, we present a novel global descriptor termed stable triangle descriptor (STD) for 3D place recognition. For a triangle, its shape is uniquely determined by the length of the sides or included angles. Moreover, the shape of triangles is completely invariant to rigid transformations. Based on this property, we first design an algorithm to efficiently extract local key points from the 3D point cloud and encode these key points into triangular descriptors. Then, place recognition is achieved by matching the side lengths (and some other information) of the descriptors between point clouds. The point correspondence obtained from the descriptor matching pair can be further used in geometric verification, which greatly improves the accuracy of place recognition. In our experiments, we extensively compare our proposed system against other state-of-the-art systems (i.e., M2DP, Scan Context) on public datasets (i.e., KITTI, NCLT, and Complex-Urban) and our self-collected dataset (with a non-repetitive scanning solid-state LiDAR). All the quantitative results show that STD has stronger adaptability and a great improvement in precision over its counterparts. To share our findings and make contributions to the community, we open source our code on our GitHub: github.com/hku-mars/STD.