{LIO}-{MARS}: Non-uniform Continuous-time Trajectories for Real-time {LiDAR}-Inertial-Odometry

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.