LIO-MARS：面向实时激光雷达-惯性里程计的非均匀连续时间轨迹
LIO-MARS: Non-uniform Continuous-time Trajectories for Real-time LiDAR-Inertial-Odometry
AI总结：
针对搜救等场景下机器人实时鲁棒感知需求，基于MARS提出LIO-MARS激光雷达-惯性里程计，采用非均匀连续时间B样条轨迹等技术，经多类数据集验证性能达当前最优。
摘要：
自主机器人系统高度依赖环境知识实现安全导航。在搜救场景中，飞行机器人需要由互补传感器支撑的鲁棒实时感知能力，其中IMU数据约束加速度与旋转，LiDAR则测量机器人周围的精确距离。本研究在LiDAR里程计MARS的基础上，提出激光雷达-惯性里程计（LIO）方法，采用连续时间B样条轨迹，结合高斯混合模型（GMM）联合对齐多分辨率surfel地图。新型扫描窗口采用非均匀时间节点放置策略，无需额外扫描延迟即可保证整条轨迹的连续性；此外，利用克罗内克和与克罗内克积将核心协方差与GMM计算加速3.3倍。无迹变换用于surfel去畸变，扫描内分段拆分则助力样条优化过程中的运动补偿；相对位姿与预积分IMU伪测量的互补软约束进一步提升了鲁棒性与精度。大量评估表明，在各类手持、地面车辆、空中车辆数据集上，本研究提出的LIO-MARS相比近期LIO系统达到了当前最优性能。
英文摘要：
Autonomous robotic systems heavily rely on environment knowledge to safely navigate. For search & rescue, a flying robot requires robust real-time perception, enabled by complementary sensors. IMU data constrains acceleration and rotation, whereas LiDAR measures accurate distances around the robot. Building upon the LiDAR odometry MARS, our LiDAR-inertial odometry (LIO) jointly aligns multi-resolution surfel maps with a Gaussian mixture model (GMM) using a continuous-time B-spline trajectory. Our new scan window uses non-uniform temporal knot placement to ensure continuity over the whole trajectory without additional scan delay. Moreover, we accelerate essential covariance and GMM computations with Kronecker sums and products by a factor of 3.3. An unscented transform de-skews surfels, while a splitting into intra-scan segments facilitates motion compensation during spline optimization. Complementary soft constraints on relative poses and preintegrated IMU pseudo-measurements further improve robustness and accuracy. Extensive evaluation showcases the state-of-the-art quality of our LIO-MARS w.r.t. recent LIO systems on various handheld, ground and aerial vehicle-based datasets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
