{"id":32196,"date":"2026-01-21T17:01:29","date_gmt":"2026-01-21T17:01:29","guid":{"rendered":"https:\/\/lamarr-institute.org\/publication\/lio-mars-non-uniform-continuous-time-trajectories-for-real-time-lidar-inertial-odometry\/"},"modified":"2026-06-08T13:18:27","modified_gmt":"2026-06-08T13:18:27","slug":"lio-mars-non-uniform-continuous-time-trajectories-for-real-time-lidar-inertial-odometry","status":"publish","type":"publication","link":"https:\/\/lamarr-institute.org\/de\/publication\/lio-mars-non-uniform-continuous-time-trajectories-for-real-time-lidar-inertial-odometry\/","title":{"rendered":"{LIO}-{MARS}: Non-uniform Continuous-time Trajectories for Real-time {LiDAR}-Inertial-Odometry"},"content":{"rendered":"<p>Autonomous robotic systems heavily rely on environment knowledge to safely navigate. For search \\&#038; 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.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Autonomous robotic systems heavily rely on environment knowledge to safely navigate. For search \\&#038; 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 [&hellip;]<\/p>\n","protected":false},"author":12,"featured_media":0,"template":"","meta":{"_acf_changed":false,"footnotes":""},"publication-type":[30],"class_list":["post-32196","publication","type-publication","status-publish","hentry","publication-type-article"],"acf":[],"publishpress_future_workflow_manual_trigger":{"enabledWorkflows":[]},"_links":{"self":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication\/32196","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication"}],"about":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/types\/publication"}],"author":[{"embeddable":true,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/users\/12"}],"version-history":[{"count":0,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication\/32196\/revisions"}],"wp:attachment":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/media?parent=32196"}],"wp:term":[{"taxonomy":"publication-type","embeddable":true,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication-type?post=32196"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}