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Updated: Jan 16, 2026

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring

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

Hyunmin Kim¹, Minkyung Jun¹, Hoeryong Jung¹

¹Department of Mechanical Engineering, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, Republic of Korea.

Sensors (Basel, Switzerland)

|September 27, 2025

Summary

This summary is machine-generated.

This study introduces a new RGB-LiDAR fusion framework for real-time drivable area detection in autonomous robots. The adaptable system achieves high accuracy and speed, enhancing robot navigation in complex environments.

Keywords:

RGB–LiDAR fusion drivable area segmentation multimodal perception real-time mapping

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Area of Science:

Robotics
Computer Vision
Sensor Fusion

Background:

Autonomous robots require accurate drivable area detection for navigation.
Existing RGB and LiDAR methods have limitations in varying conditions and sensor configurations.
Current fusion systems often lack adaptability to hardware changes.

Purpose of the Study:

To present a real-time, modular RGB-LiDAR fusion framework for robust drivable area recognition and mapping.
To enable sensor-agnostic adaptability without retraining for diverse robotic platforms.
To improve the reliability and efficiency of autonomous navigation systems.

Main Methods:

Decoupled RGB and LiDAR preprocessing for sensor-agnostic adaptability.
Fusion of RGB segmentation with LiDAR ground estimation for high-confidence drivable area point clouds.
Incremental integration of point clouds via Simultaneous Localization and Mapping (SLAM) into a global map.

Main Results:

The framework achieves competitive accuracy in drivable area detection on the KITTI dataset.
Demonstrates the highest inference speed among compared methods.
Validates suitability for real-time autonomous navigation.

Conclusions:

The proposed RGB-LiDAR fusion framework offers robust and adaptable drivable area recognition.
The system's modularity and sensor-agnostic design facilitate seamless deployment across platforms.
High accuracy and speed confirm its effectiveness for real-time autonomous navigation.