Sequential Autonomous Exploration-Based Precise Mapping for Mobile Robots through Stepwise and Consistent Motions

This paper proposes a 2-D autonomous exploration and mapping framework for LiDAR-based SLAM mobile robots, designed to address the major challenges on low-cost platforms, including process instability, map drift, and increased risks of collisions and deadlocks. For frontier search, the local-global sampling architecture based on Rapidly-exploring Random Trees (RRTs) is employed. For local exploration, the proposed Self-Convergent RRT (SC-RRT) efficiently covers the reachable space within a finite time while the robot remains stationary, without relying on motion-induced sampling diversity. In addition, traversability checks during RRT expansion and global RRT pruning upon map updates eliminate unreachable frontiers, reducing potential collisions and deadlocks. For frontier point navigation, a stepwise consistent motion strategy is employed to generate motion trajectories that are more amenable to stable scan matching. The resulting straight-segment and in-place-rotation pattern improves scan-matching robustness and effectively suppresses map drift on resource-constrained platforms. For the process control, the framework serializes frontier point selection and navigation, avoiding oscillations caused by frequent goal changes in conventional parallelized processes. The waypoint retracing mechanism is incorporated to generate repeated observations, triggering loop closure detection and backend optimization in graph-based SLAM, thereby improving map consistency. Experiments in challenging simulated and real-world environments validate the effectiveness of the framework. Compared with baseline methods, the proposed framework achieves higher mapping success rates and stronger robustness on resource-constrained robots and maintains consistent mapping quality across various LiDAR field-of-view (FoV) configurations.