Evaluation of 3D CNN Semantic Mapping for Rover Navigation [article]

Sebastiano Chiodini, Luca Torresin, Marco Pertile, Stefano Debei
2020 arXiv   pre-print
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
more » ... 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.
arXiv:2006.09761v1 fatcat:p6lwg56skrhrnhcienhy4odjju