Fig. 1: Object detection using deep learning and grid fusion. Abstract-We tackle the problem of object detection and pose estimation in a shared space downtown environment. For perception multiple laser scanners with 360 • coverage were fused in a dynamic occupancy grid map (DOGMa). A singlestage deep convolutional neural network is trained to provide object hypotheses comprising of shape, position, orientation and an existence score from a single input DOGMa. Furthermore, an algorithm for offline object extraction was developed to automatically label several hours of training data. The algorithm is based on a two-pass trajectory extraction, forward and backward in time. Typical for engineered algorithms, the automatic label generation suffers from misdetections, which makes hard negative mining impractical. Therefore, we propose a loss function counteracting the high imbalance between mostly static background and extremely rare dynamic grid cells. Experiments indicate, that the trained network has good generalization capabilities since it detects objects occasionally lost by the label algorithm. Evaluation reaches an average precision (AP) of 75.9 %. IEEE Intelligent Vehicles Symposium (IV)
Hoermann REF employs temporal consistency to generate labels by processing data both forwards and backwards in time.
Object Detection on Dynamic Occupancy Grid Maps Using Deep Learning and Automatic Label Generation
2018 IEEE Intelligent Vehicles Symposium (IV)
2018 IEEE Intelligent Vehicles Symposium (IV)
Computer Science