LT-mapper: A Modular Framework for LiDAR-based Lifelong Mapping

Abstract

Long-term 3D map management is a fundamental capability required by a robot to reliably navigate in the non-stationary real-world. This paper develops open-source, modular, and readily available LiDAR-based lifelong mapping for urban sites. This is achieved by dividing the problem into successive subproblems: multi-session SLAM (MSS), high/low dynamic change detection, and positive/negative change management. The proposed method leverages MSS and handles potential trajectory error; thus, good initial alignment is not required for change detection. Our change management scheme preserves efficacy in both memory and computation costs, providing automatic object segregation from a large-scale point cloud map. We verify the framework's reliability and applicability even under permanent year-level variation, through extensive real-world experiments with multiple temporal gaps (from day to year).

Wildcat: Online Continuous-Time 3D Lidar-Inertial SLAM

“…In particular, LIO-SAM is a tightly-coupled keyframe-based online system that combines lidar odometry with IMU preintegration, loop closures, and (if available) GPS factors via posegraph optimisation. LT-Mapper [14] builds on LIO-SAM and uses Scan Context (SC) [15] for loop closure detection. We compare Wildcat with LIO-SAM in Sec.…”