The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B2-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 637–644, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-637-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 637–644, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-637-2020

  12 Aug 2020

12 Aug 2020

SEMANTIC SCENE UNDERSTANDING FOR THE AUTONOMOUS PLATFORM

B. Vishnyakov, Y. Blokhinov, I. Sgibnev, V. Sheverdin, A. Sorokin, A. Nikanorov, P. Masalov, K. Kazakhmedov, S. Brianskiy, Е. Andrienko, and Y. Vizilter B. Vishnyakov et al.
  • 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.