Multimodal RGB-LiDAR Fusion for Robust Drivable Area Segmentation and Mapping.

ABSTRACT: Drivable area detection and segmentation are critical tasks for autonomous mobile robots in complex and dynamic environments. RGB-based methods offer rich semantic information but suffer in unstructured environments and under varying lighting, while LiDAR-based models provide precise spatial measurements but often require high-resolution sensors and are sensitive to sparsity. In addition, most fusion-based systems are constrained by fixed sensor setups and demand retraining when hardware configurations change. This paper presents a real-time, modular RGB-LiDAR fusion framework for robust drivable area recognition and mapping. Our method decouples RGB and LiDAR preprocessing to support sensor-agnostic adaptability without retraining, enabling seamless deployment across diverse platforms. By

SUBMITTER: Kim H 

PROVIDER: S-EPMC12473224 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature