LIO-MARS用非均匀时间轨迹实现高精度实时激光惯性里程计。
LIO-MARS: Non-uniform Continuous-time Trajectories for Real-time LiDAR-Inertial-Odometry
- 基于非均匀B样条轨迹，融合多分辨率表面贴图与高斯混合模型
- 计算加速3.3倍，支持无延迟扫描窗口，误差低于0.15%
- 适合无人机、机器人等需要实时精准定位的场景
自主机器人系统依赖环境感知以安全导航。在搜救任务中，飞行机器人需具备鲁棒的实时感知能力，依赖互补传感器：IMU约束加速度与角速度，而LiDAR测量机器人周围的精确距离。在MARS激光里程计基础上，本文提出的激光惯性里程计（LIO）采用连续时间B样条轨迹，联合对齐多分辨率surfel地图与高斯混合模型（GMM）。新设计的扫描窗口采用非均匀时间节点分布，确保整个轨迹连续且无需额外扫描延迟。此外，通过克罗内克和与积加速关键协方差及GMM计算，提速3.3倍。无迹变换消除surfel偏斜，将扫描分段处理以提升样条优化中的运动补偿效果。相对位姿与预积分IMU伪观测的软约束进一步增强系统鲁棒性与精度。大量实验表明，LIO-MARS在手持、地面及空中平台数据集上均达到当前最优性能，优于近期主流LIO系统。
原文摘要 · Abstract (English)
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.
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