Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks
Detailed environment perception is a crucial component of automated vehicles. However, to deal with the amount of perceived information, we also require segmentation strategies. Based on a grid map environment representation, well suited for sensor fusion, free space estimation and machine learning, we detect and classify objects using deep convolutional neural networks. As input for our networks we use a multi layer grid map efficiently encoding 3D range sensor information. The inference output consists of a list of rotated bounding boxes with associated semantic classes. We conduct extensive ablation studies, highlight important design considerations when using grid maps and evaluate our models on the KITTI Bird's Eye View benchmark. Qualitative and quantitative benchmark results show that we achieve robust detection and state of the art accuracy solely using top view grid maps from range sensor data.
2018 21st International Conference on Intelligent Transportation Systems (ITSC)