Vision-Based Obstacle Detection for Mobile Robot in Outdoor Environment

Abstract

This paper proposes a vision-based obstacle detection method for a mobile robot operating in an outdoor environment. The method utilizes a single camera to capture images of the robot's surroundings. The captured images are then processed to identify and locate obstacles in real-time. The proposed method employs a combination of image processing techniques, including edge detection, feature extraction, and machine learning algorithms, to accurately detect various types of obstacles such as rocks, trees, and uneven terrain. Experimental results demonstrate the effectiveness and robustness of the proposed system in diverse outdoor scenarios.