Mixed-Domain Training Improves Multi-mission Terrain Segmentation

Vincent, Grace E.; Yepremyan, Alice; Chen, Jingdao; Goh, Edwin

doi:10.1007/978-3-031-25056-9_7

Cited by 3 publications

(1 citation statement)

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“…Meanwhile, some studies have acquired scene data from sensors [11,12] for semantic segmentation [13,14] to obtain semantic maps [15]. For example, Hosseinpoor et al [16] use aerial RGB images to segment images by different threshold values of height.…”

Section: Introductionmentioning

confidence: 99%

TTA-GEP: Terrain Traversability Analysis with Geometry and Environmental Perception for the Path Planning of Planetary Rovers

Yang,

Liang,

et al. 2023

International Journal of Aerospace Engineering

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Terrain traversability analysis (TTA), the key to the navigation of planetary rovers, is significant to the safety of the rover. Therefore, owing to its complexity, the Martian terrain is worth analysing comprehensively based on the terrain variability and hazard level. In this work, we propose a novel method for terrain traversability analysis for the path planning of planetary rovers by integrating Martian terrain geometry features with terrain semantic information, which includes geometry and environmental perception (GEP). Specifically, we deploy semantic segmentation to classify common terrain types, such as rocks, bedrocks, and sand, obtaining semantic information as one part of terrain traversability analysis at the same time. Simultaneously, the point cloud is generated by using binocular images from the planetary rover navigation camera (Navcam) to construct a 2.5D elevation map of the environment to analyse the geometric characteristics of the terrain. Besides, we implement path planning based on the results of TTA-GEP. Overall, our proposed method improves the performance of the terrain traversability analysis and reduces the risk of planetary rovers while detecting in an unstructured environment.

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