Evaluation of 3D CNN Semantic Mapping for Rover Navigation
This work addresses terrain assessment for autonomous rovers, but it appears incremental as it applies existing methods to a specific domain.
The paper tackled the problem of generating accurate 3D semantic maps for Martian rover navigation by combining DeepLabv3+ semantic segmentation with stereo depth maps in a voxel representation, achieving evaluation on the ESA Katwijk Beach dataset.
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.