Camera-based Obstacle Classification for Automated Reach Trucks Using Deep Learning

This paper focuses on the classification of obstacles that are widely present in warehouse environments using an RGBD camera. Our approach applies depth segmentation to detect obstacles which are classified using a Convolutional Neural Network and a Support Vector Machine. Our system is evaluated on real-world data captured from an automated reach truck in a warehouse environment.