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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.

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.