Occupancy Grid Generation With Dynamic Obstacle Segmentation in Stereo Images

The detection of dynamic and static obstacles is a key task for the navigation of autonomous ground vehicles. The article presents a new algorithm for generating an occupancy map of the surrounding space from noisy point clouds obtained from one or several stereo cameras. The camera images are segmented by the proposed deep neural network FCN-ResNet-M-OC, which combines the speed of the FCN-ResNet method and improves the quality of the model using the concept of object context representation. The paper investigates supervised approaches to network training on unbalanced samples with road scenes such as the weighted cross entropy and the Focal Loss. The occupancy map is built from point clouds with semantic labels, in which static environment and potentially dynamic obstacles are highlighted. Our solution is operational in real time and applicable on platforms with limited computing resources. The approach was tested on autonomous vehicle datasets: Semantic KITTI, KITTI-360, Mapillary Vistas and custom OpenTaganrog. The usage of semantically labeled point clouds increased the precision of obstacle detection by an average of 17%. The performance of the entire approach on various computing platforms with Jetson Xavier, RTX3070, GPUs NVidia Tesla V100 is respectively from 10 to 15 FPS for input image resolution $1920\times 1080$ pixels.

I. Introduction

To navigate safely and efficiently in areas with a large number of dynamic objects, unmanned ground vehicles (UGV) must synthesize a map of the surrounding space in real time. In this case, objects should be divided into static and potentially dynamic, with the latter category requiring more attention.