论文标题
评估Rover导航的3D CNN语义映射
Evaluation of 3D CNN Semantic Mapping for Rover Navigation
论文作者
论文摘要
地形评估是自主探索流浪者的关键方面，周围环境识别需要多种目的，例如最佳轨迹计划和自主目标识别。在这项工作中，我们提出了一种为火星环境生成准确的三维语义图的技术。该算法用作输入的立体声图像，该立体声图像由安装在流动站上的摄像机获取。首先，图像用DeepLabv3+标记，这是一个编码器卷积神经Networl（CNN）。然后，通过语义分割获得的标签合并到体素表示中的立体深度图。我们在ESA Katwijk Beach Planetary Rover数据集上评估了我们的方法。
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.
