Uploaded September 2022 | Updated September 2026, 3 weeks ago
This work presents a method for tightly-coupled LiDAR-inertial SLAM utilizing factor graphs as the underlying representation. The method extracts LOAM-style features and performs a scan to scan registration step, while also incorporating preintegrated IMU constraints within its optimization. A scan to submap registration step follows, which instead of using a monolithic map, utilizes a dynamic submap built using spatial keyframes over a sliding window, thus enabling past registration errors (within the keyframe window) to be corrected given new observations. Incremental fixed lag smoothing is performed over the maintained factor graphs in the individual scan to scan and scan to submap optimization steps to ensure bounded computational complexity. The method is evaluated on datasets collected with an aerial robot inside subterranean environments and an industrial facility.
This work presents a method for tightly-coupled LiDAR-inertial SLAM utilizing factor graphs as the underlying representation. The method extracts LOAM-style features and performs a scan to scan registration step, while also incorporating preintegrated IMU constraints within its optimization. A scan to submap registration step follows, which instead of using a monolithic map, utilizes a dynamic submap built using spatial keyframes over a sliding window, thus enabling past registration errors (within the keyframe window) to be corrected given new observations. Incremental fixed lag smoothing is performed over the maintained factor graphs in the individual scan to scan and scan to submap optimization steps to ensure bounded computational complexity. The method is evaluated on datasets collected with an aerial robot inside subterranean environments and an industrial facility.
