Point cloud maps with accurate color are crucial in robotics and mapping applications. Existing approaches for producing RGB-colorized maps are primarily based on realtime localization using filter-based estimation or sliding window optimization, which may lack accuracy and global consistency. In this work, we introduce a novel global LiDAR-Visual bundle adjustment (BA) named LVBA to improve the quality of RGB point cloud mapping beyond existing baselines. LVBA first optimizes LiDAR poses via a global LiDAR BA, followed by a photometric visual BA incorporating planar features from the LiDAR point cloud for camera pose optimization. Additionally, to address the challenge of map point occlusions in constructing optimization problems, we implement a novel LiDAR-assisted global visibility algorithm in LVBA. To evaluate the effectiveness of LVBA, we conducted extensive experiments by comparing its mapping quality against existing state-of-the-art baselines (i.e.,$\mathbf{R}^{3}$LIVE and FAST-LIVO). Our results prove that LVBA can proficiently reconstruct high-fidelity, accurate RGB point cloud maps, outperforming its counterparts.

This paper presents a unified surface reconstruction and rendering framework for LiDAR-visual systems, integrating Neural Radiance Fields (NeRF) and Neural Distance Fields (NDF) to recover both appearance and structural information from posed images and point clouds. We address the structural visible gap between NeRF and NDF by utilizing a visible-aware occupancy map to classify space into the free, occupied, visible unknown, and background regions. This classification facilitates the recovery of a complete appearance and structure of the scene. We unify the training of the NDF and NeRF using a spatial-varying scale SDF-to-density transformation for levels of detail for both structure and appearance. The proposed method leverages the learned NDF for structure-aware NeRF training by an adaptive sphere tracing sampling strategy for accurate structure rendering. In return, NeRF further refines structural in recovering missing or fuzzy structures in the NDF. Extensive experiments demonstrate the superior quality and versatility of the proposed method across various scenarios. To benefit the community, the codes will be released at https://github.com/hku-mars/M2Mapping.

Navigating an arbitrary-shaped ground robot safely in cluttered environments remains a challenging problem. The existing trajectory planners that account for the robot’s physical geometry severely suffer from the intractable runtime. To achieve both computational efficiency and Continuous Collision Avoidance (CCA) of arbitrary-shaped ground robot planning, we proposed a novel coarse-to-fine navigation framework that significantly accelerates planning. In the first stage, a sampling-based method selectively generates distinct topological paths that guarantee a minimum inflated margin. In the second stage, a geometry-aware front-end strategy is designed to discretize these topologies into full-state robot motion sequences while concurrently partitioning the paths into SE(2) sub-problems and simpler ℝ2sub-problems for back-end optimization. In the final stage, an SVSDF-based optimizer generates trajectories tailored to these sub-problems and seamlessly splices them into a continuous final motion plan. Extensive benchmark comparisons show that the proposed method is one to several orders of magnitude faster than the cutting-edge methods in runtime while maintaining a high planning success rate and ensuring CCA.

Previous dominant methods for scene flow estimation focus mainly on input from two consecutive frames, neglecting valuable information in the temporal domain. While recent trends shift towards multi-frame reasoning, they suffer from rapidly escalating computational costs as the number of frames grows. To leverage temporal information more efficiently, we propose DeltaFlow ($\Delta$Flow), a lightweight 3D framework that captures motion cues via a $\Delta$ scheme, extracting temporal features with minimal computational cost, regardless of the number of frames. Additionally, scene flow estimation faces challenges such as imbalanced object class distributions and motion inconsistency. To tackle these issues, we introduce a Category-Balanced Loss to enhance learning across underrepresented classes and an Instance Consistency Loss to enforce coherent object motion, improving flow accuracy. Extensive evaluations on the Argoverse 2, Waymo and nuScenes datasets show that $\Delta$Flow achieves state-of-the-art performance with up to 22\% lower error and $2\times$ faster inference compared to the next-best multi-frame supervised method, while also demonstrating a strong cross-domain generalization ability. The code is open-sourced at https://github.com/Kin-Zhang/DeltaFlow along with trained model weights.

Mobile robots have revolutionized various fields, offering solutions for manipulation, environmental monitoring, and exploration. However, payload capacity remains a limitation. This paper presents a novel thrust-based robotic hopper capable of carrying payloads up to 9 times its own weight while maintaining agile mobility over less structured terrain. The 220 gram robot carries up to 2 kg while hopping—–a capability that bridges the gap between high-payload ground robots and agile aerial platforms. Key advancements that enable this high-payload capacity include the integration of bidirectional thrusters, allowing for both upward and downward thrust generation to enhance energy management while hopping. Additionally, we present a refined model of dynamics that accounts for heavy payload conditions, particularly for large jumps. To address the increased computational demands, we employ a neural network compression technique, ensuring real-time onboard control. The robot's capabilities are demonstrated through a series of experiments, including leaping over a high obstacle, executing sharp turns with large steps, as well as performing simple autonomous navigation while carrying a 730 g LiDAR payload. This showcases the robot's potential for applications such as mobile sensing and mapping in challenging environments.

Trajectory generation for fully autonomous flights of tail-sitter unmanned aerial vehicles (UAVs) presents substantial challenges due to their highly nonlinear aerodynamics. In this paper, we introduce, to the best of our knowledge, the world's first fully autonomous tail-sitter UAV capable of high-speed navigation in unknown, cluttered environments. The UAV autonomy is enabled by cutting-edge technologies including LiDAR-based sensing, differential-flatness-based trajectory planning and control with purely onboard computation. In particular, we propose an optimization-based tail-sitter trajectory planning framework that generates high-speed, collision-free, and dynamically-feasible trajectories. To efficiently and reliably solve this nonlinear, constrained problem, we develop an efficient feasibility-assured solver, EFOPT, tailored for the online planning of tail-sitter UAVs. We conduct extensive simulation studies to benchmark EFOPT's superiority in planning tasks against conventional NLP solvers. We also demonstrate exhaustive experiments of aggressive autonomous flights with speeds up to 15m/s in various real-world environments, including indoor laboratories, underground parking lots, and outdoor parks. A video demonstration is available athttps://youtu.be/OvqhlB2h3k8, and the EFOPT solver is open-sourced athttps://github.com/hku-mars/EFOPT.

Aerial swarm systems possess immense potential in various aspects, such as cooperative exploration, target tracking, and search and rescue. Efficient accurate self- and mutual state estimation are the critical preconditions for completing these swarm tasks, which remain challenging research topics. This article proposes Swarm-LIO2, a fully decentralized, plug-and-play, computationally efficient, and bandwidth-efficient light detection and ranging (LiDAR)-inertial odometry for aerial swarm systems. Swarm-LIO2 uses a decentralized plug-and-play network as the communication infrastructure. Only bandwidth-efficient and low-dimensional information is exchanged, including identity, ego state, mutual observation measurements, and global extrinsic transformations. To support the plug and play of new teammate participants, Swarm-LIO2 detects potential teammate autonomous aerial vehicles (AAVs) and initializes the temporal offset and global extrinsic transformation all automatically. To enhance the initialization efficiency, novel reflectivity-based AAV detection, trajectory matching, and factor graph optimization methods are proposed. For state estimation, Swarm-LIO2 fuses LiDAR, inertial measurement units, and mutual observation measurements within an efficient error state iterated Kalman filter (ESIKF) framework, with careful compensation of temporal delay and modeling of measurements to enhance the accuracy and consistency. Moreover, the proposed ESIKF framework leverages the global extrinsic for ego state estimation in the case of LiDAR degeneration or refines the global extrinsic along with the ego state estimation otherwise. To enhance the scalability, Swarm-LIO2 introduces a novel marginalization method in the ESIKF, which prevents the growth of computational time with swarm size. Extensive simulation and real-world experiments demonstrate the broad adaptability to large-scale aerial swarm systems and complicated scenarios, including GPS-denied scenes and degenerated scenes for cameras or LiDARs. The experimental results showcase the centimeter-level localization accuracy, which outperforms other state-of-the-art LiDAR-inertial odometry for a single-AAV system. Furthermore, diverse applications demonstrate the potential of Swarm-LIO2 to serve as a reliable infrastructure for various aerial swarm missions.

Recent advances in LiDAR technology have opened up new possibilities for robotic navigation. Given the widespread use of occupancy grid maps (OGMs) in robotic motion planning, this paper aims to address the challenges of integrating LiDAR with OGMs. To this end, we propose ROG-Map, a uniform grid-based OGM that maintains a local map moving along with the robot to enable efficient map operation and reduce memory costs for large-scene autonomous flight. Moreover, we present a novel incremental obstacle inflation method that significantly reduces the computational cost of inflation. The proposed method outperforms state-of-the-art methods on various public datasets. To demonstrate the effectiveness and efficiency of ROG-Map, we integrate it into a complete quadrotor system and perform autonomous flights against both small obstacles and large-scale scenes. During real-world flight tests with a 0.05 m resolution local map and 30 m×30 m×6 m local map size, ROG-Map takes only 29.8 % of frame time on average to update the map at a frame rate of 50 Hz (i.e., 5.96 ms in 20 ms), including 0.33 % (i.e., 0.66 ms) to perform obstacle inflation, which represents only half of the total map updating time when compared to the state-of-the-art baseline. We release ROG-Map as an open-source ROS package1to promote the development of LiDAR-based motion planning.

Occupancy mapping is a fundamental component of robotic systems to reason about the unknown and known regions of the environment. This article presents an efficient occupancy mapping framework for high-resolution light detection and ranging (LiDAR) sensors, termed D-Map. The framework introduces three main novelties to address the computational efficiency challenges of occupancy mapping. First, we use a depth image to determine the occupancy state of regions instead of the traditional ray-casting method. Second, we introduce an efficient on-tree update strategy on a tree-based map structure. These two techniques avoid redundant visits to small cells, significantly reducing the number of cells to be updated. Third, we remove known cells from the map at each update by leveraging the low false alarm rate of LiDAR sensors. This approach not only enhances our framework's update efficiency by reducing map size but also endows it with an interesting decremental property, which we have named D-Map. To support our design, we provide theoretical analyzes of the accuracy of the depth image projection and time complexity of occupancy updates. Furthermore, we conduct extensive benchmark experiments on various LiDAR sensors in both public and private datasets. Our framework demonstrates superior efficiency in comparison with other state-of-the-art methods while maintaining comparable mapping accuracy and high memory efficiency. We demonstrate two real-world applications of D-Map for real-time occupancy mapping on a handheld device and an aerial platform carrying a high-resolution LiDAR.

In this article, we propose a novel light detection and ranging (LiDAR)(-inertial) odometry and mapping framework to achieve the goal of simultaneous localization and meshing in real time. This proposed framework termed immediately meshing (ImMesh) comprises four tightly-coupled modules: receiver, localization, meshing, and broadcaster. The localization module first utilizes the preprocessed sensor data from the receiver, estimates the sensor pose online by registering LiDAR scans to maps, and dynamically grows the map. Then, our meshing module takes the registered LiDAR scan for incrementally reconstructing the triangle mesh on the fly. Finally, the real-time odometry, map, and mesh are published via our broadcaster. The primary contribution of this work is the meshing module, which represents a scene by an efficient voxel structure, performs fast finding of voxels observed by new scans, and incrementally reconstructs triangle facets in each voxel. This voxel-wise meshing operation is delicately designed for the purpose of efficiency; it first performs a dimension reduction by projecting 3-D points to a 2-D local plane contained in the voxel, and then executes the meshing operation with pull, commit, and push steps for incremental reconstruction of triangle facets. To the best of authors' knowledge, this is the first work in the literature that can reconstruct online the triangle mesh of large-scale scenes, just relying on a standard CPU without GPU acceleration.

This article presents FAST-LIO2: a fast, robust, and versatile LiDAR-inertial odometry framework. Building on a highly efficient tightly coupled iterated Kalman filter, FAST-LIO2 has two key novelties that allow fast, robust, and accurate LiDAR navigation (and mapping). The first one is directly registering raw points to the map (and subsequently update the map, i.e., mapping) without extracting features. This enables the exploitation of subtle features in the environment and, hence, increases the accuracy. The elimination of a hand-engineered feature extraction module also makes it naturally adaptable to emerging LiDARs of different scanning patterns; the second main novelty is maintaining a map by an incremental k-dimensional (k-d) tree data structure, incremental k-d tree (ikd-Tree), that enables incremental updates (i.e., point insertion and delete) and dynamic rebalancing. Compared with existing dynamic data structures (octree, R$^\ast$-tree, andnanoflannk-d tree),ikd-Treeachieves superior overall performance while naturally supports downsampling on the tree. We conduct an exhaustive benchmark comparison in 19 sequences from a variety of open LiDAR datasets. FAST-LIO2 achieves consistently higher accuracy at a much lower computation load than other state-of-the-art LiDAR-inertial navigation systems. Various real-world experiments on solid-state LiDARs with small field of view are also conducted. Overall, FAST-LIO2 is computationally efficient (e.g., up to 100 Hz odometry and mapping in large outdoor environments), robust (e.g., reliable pose estimation in cluttered indoor environments with rotation up to 1000 deg/s), versatile (i.e., applicable to both multiline spinning and solid-state LiDARs, unmanned aerial vehicle (UAV) and handheld platforms, and Intel- and ARM-based processors), while still achieving a higher accuracy than existing methods. Our implementation of the system FAST-LIO2 and the data structureikd-Treeare both open-sourced on Github.