SEMANTIC SCENE UNDERSTANDING FOR THE AUTONOMOUS PLATFORM
- FGUP «State Research Institute of Aviation Systems», Russia, 125319, Moscow, Viktorenko street, 7
Keywords: multi-sensor platform, autonomous vehicle, SLAM, CNN, dynamic scene analysis, semantic segmentation, off-road, autonomous driving, camera calibration, LiDAR calibration
Abstract. In this paper we describe a new multi-sensor platform for data collection and algorithm testing. We propose a couple of methods for solution of semantic scene understanding problem for land autonomous vehicles. We describe our approaches for automatic camera and LiDAR calibration; three-dimensional scene reconstruction and odometry calculation; semantic segmentation that provides obstacle recognition and underlying surface classification; object detection; point cloud segmentation. Also, we describe our virtual simulation complex based on Unreal Engine, that can be used for both data collection and algorithm testing. We collected a large database of field and virtual data: more than 1,000,000 real images with corresponding LiDAR data and more than 3,500,000 simulated images with corresponding LiDAR data. All proposed methods were implemented and tested on our autonomous platform; accuracy estimates were obtained on the collected database.