Abstract:

The 3D object recognition based on lidar data is a key part of autopilot system. Voxel network is a good container for extracting point cloud features， but most of the research at present on object recognition based on voxel network focuses on single?category object. In order to meet the application demand of unmanned vehicle， it is urgent to carry out research on multi?category object recognition. In this paper， a multi?category object recognition algorithm based on voxel network is established and its performance is validated. The category label， confidence label and bounding borders regression values of the voxels around the tag are created by calculating the maximal intersection over union（IoU） among prior candidate borders of all categories simultaneously， which resolves the possible mismatch among the three predicted values. The test results indicate that the average recall of category prediction of the proposed multi?category object recognition algorithm is 88.6% and taking the IoU threshold of 0.5 as the correct one， the border regression is 84.8%. Compared with the single?category object recognition network， each category performs an obviously improved accuracy using the proposed algorithm， which proves that the multi?category object recognition algorithm effectively enhances the ability of characteristics learning， and contributes to the improvement of the robustness of the object recognition network.

Key words: object recognition, multi?category, voxel network, lidar, robustness