Evaluation of 3D CNN Semantic Mapping for Rover Navigation

• URL: http://arxiv.org/abs/2006.09761v1
• Date: Wed, 17 Jun 2020 10:24:29 GMT
• Title: Evaluation of 3D CNN Semantic Mapping for Rover Navigation
• Authors: Sebastiano Chiodini, Luca Torresin, Marco Pertile, Stefano Debei
• Abstract summary: We present a technique to generate accurate three-dimensional semantic maps for Martian environment. The algorithm uses as input a stereo image acquired by a camera mounted on a rover. We evaluate our approach on the ESA Katwijk Beach Planetary Rover dataset.
• Score: 0.6882042556551609
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Terrain assessment is a key aspect for autonomous exploration rovers, surrounding environment recognition is required for multiple purposes, such as optimal trajectory planning and autonomous target identification. In this work we present a technique to generate accurate three-dimensional semantic maps for Martian environment. The algorithm uses as input a stereo image acquired by a camera mounted on a rover. Firstly, images are labeled with DeepLabv3+, which is an encoder-decoder Convolutional Neural Networl (CNN). Then, the labels obtained by the semantic segmentation are combined to stereo depth-maps in a Voxel representation. We evaluate our approach on the ESA Katwijk Beach Planetary Rover Dataset.

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