Low-height Obstacle Detection and Avoidance by 3D Geometric Method and Deep Neural Network

Abstract

In this study, we present the development of a new method to detect unknown low-height obstacles using 3D point clouds captured by stereo cameras. Conventional semantic segmentation methods using a depth image by a deep neural network (DNN) can detect road surfaces with a high accuracy. However, it is difficult to detect unknown low-height obstacles that are not included in the training data. Methods that use 3D geometric information, such as the normal and height face difficulty in detecting objects with a surface that is parallel to the road surface and low objects, respectively. Therefore, the objective of this study is to address the difficult problem of detecting unknown low-height obstacles by focusing on the difference in the difficult obstacle detection between the DNN and 3D geometric methods. Based on the confidence from the output of the DNN, we accomplish difficult obstacle detection with the DNN by using 3D geometric information, and vice versa. When tested on a robot equipped with a stereo camera, the intersection over union (IoU), which indicates the detection accuracy of the unknown obstacles, was improved by 18.6 %age points compared to that with the DNN. Moreover, our method enabled the robot to safely avoid three types of unknown low-height obstacles.