Obstacle Detection and Classification with Portable Uncalibrated Patterned Projected Light

Autonomous obstacle avoidance systems are instrumental for applications including assisting visually impaired people, guiding autonomous robots, and assisted driving. In this paper, we propose a novel method for autonomous obstacle detection and recognition, which employs a different type of sensor incorporating a patterned light with camera, and provides a lightweight, cost-conscious, energy-efficient, reliable and portable solution. Experimental results indicate that grids, projected by the patterned light source, are apparent and differentiable as the sensing system is hand-carried in both indoor and outdoor environments over and towards different types of obstacles. We propose methods for exploiting these patterns for obstacle detection and classification without requiring calibration. We demonstrate that obstacles can be classified by using our novel patterned light and camera setup and employing a Convolutional Neural Network (CNN)-based approach. The proposed method provides very promising results with overall detection and classification accuracies of 93.11% and 86.06% respectively.