# Evaluation of 3D CNN Semantic Mapping for Rover Navigation

Source: [Evaluation of 3D CNN Semantic Mapping for Rover Navigation](<https://www.sota2.com/research/paper/68fa67c81e209131cea1850d>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:22:05.587Z

Authors: Sebastiano Chiodini, Luca Torresin, Marco Pertile, Stefano Debei

Published: 2020-06-17T10:24:29.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2006.09761>)

[PDF](<https://arxiv.org/pdf/2006.09761.pdf>)