Work

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

Year: 2024

Type: article

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.

Source: Applied Sciences

DOI: 10.3390/app142411613

Authors: ChangWan Ha, DongHyun Yang, Gicheol Wang, Sung Chang Kim, HyungGi Jo

Institutions: Jeonbuk National University, Electronics and Telecommunications Research Institute

Language: English

Topic: Robotics and Sensor-Based Localization

Subfield: Aerospace Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,728

Funder: Electronics and Telecommunications Research Institute