Event-Based Slip Estimation Framework for Space Rovers Traversing Soft Terrains
Estimating terrain-induced longitudinal slip poses a significant challenge in space rover navigation, particularly when traversing soft terrains in low-light conditions. Precise estimation of this slip is important for rover navigation algorithms, as it helps to prevent rovers from traversing areas...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10613755/ |
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| Summary: | Estimating terrain-induced longitudinal slip poses a significant challenge in space rover navigation, particularly when traversing soft terrains in low-light conditions. Precise estimation of this slip is important for rover navigation algorithms, as it helps to prevent rovers from traversing areas with excessive slippage, thereby avoiding entrapment —an outcome that could lead to mission failure. This paper presents a novel event-based longitudinal slip estimation framework designed to operate in low-light conditions. Existing vision-based slip estimation techniques suffer from low temporal resolution sensors and a high sensitivity to motion blur. These limitations prevent the immediate detection of slip events, which can occur in microseconds, making such methods unsuitable for highly variable terrain environments such as lunar surfaces. Our proposed framework addresses these challenges, offering a robust solution for precise slip estimation in microseconds with limited energy consumption. Specifically, we generate the data directly from real-time wheel traces at varied light conditions and motion patterns and then propose a framework to estimate the slip. This framework consists of a denoising filter and a Hough transformer. Experiments conducted on a single-wheel test rig demonstrate the effectiveness of the proposed event-based framework in estimating slip ratios that range from 2% to 90% with an estimated accuracy of above 95% in well-lit conditions and above 94% in low-light conditions. A supplementary video is available at <uri>https://youtu.be/eac6Q2a8A9g</uri>. |
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| ISSN: | 2169-3536 |