Real-Time Drivable Region Mapping Using an RGB-D Sensor with Loop Closure Refinement and 3D Semantic Map-Merging

Abstract

Drivable region maps, created using a visual sensor, are essential for autonomous navigation because off-the-shelf maps do not reflect contemporary real-world conditions. This study presents a large-scale drivable region mapping system that is capable of capturing large-scale environments in real-time, using a single RGB-D sensor. Whereas existing semantic simultaneous localization and mapping (SLAM) methods consider only accurate pose estimation and the registration of semantic information, when loop closure is detected, contemporaneous large-scale spatial semantic maps are generated by refining 3D point clouds and semantic information. When loop closure occurs, our method finds the corresponding keyframe for each semantically labeled point cloud and transforms the point cloud into adjusted positions. Additionally, a map-merging algorithm for semantic maps is proposed to address large-scale environments. Experiments were conducted on the Complex Urban dataset and our custom dataset, which are publicly available, and real-world datasets using a vehicle-mounted sensor. Our method alleviates the drift errors that frequently occur when the agents navigate in large areas. Compared with satellite images, the resulting semantic maps are well aligned and have proven validity in terms of timeliness and accuracy.