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• W3048752751 abstract "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." @default.
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• W3048752751 date "2020-08-12" @default.
• W3048752751 modified "2023-10-18" @default.
• W3048752751 title "SEMANTIC SCENE UNDERSTANDING FOR THE AUTONOMOUS PLATFORM" @default.
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• W3048752751 doi "https://doi.org/10.5194/isprs-archives-xliii-b2-2020-637-2020" @default.
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