PRISM-Nav: Pre-SLAM Integrated Scan Merging for Autonomous Wheelchair Navigation

Abstract

Autonomous wheelchairs using 2D LiDAR suffer collisions with elevated obstacles that sit above the scan plane and remain undetected. Post-SLAM methods add camera-based 3D awareness to navigation costmaps but never update the occupancy grid map used for localization, creating map-costmap inconsistency that limits navigation robustness. We present PRISM-Nav, a pre-SLAM fusion method that merges heightfiltered depth projections from three cameras with 2D LiDAR via element-wise minimum, embedding elevated obstacles directly into the occupancy grid map before map construction. In three-way controlled trials (20 trials per configuration in each of two environments), PRISM-Nav achieved zero collisions across all 40 runs and reached 33 goals. LiDAR-only suffered 20 collisions across both sites, and the post-SLAM baseline (STVL) exhibited 3 kidnapping events. Ablation over the consensus threshold showed that this parameter controls the trade-off between spurious obstacles and missed detections. The system runs in real time (10 Hz) on CPU as a standalone ROS2 node.