| 引用本文: | 樊铭瑞,张世栋,李运,牛文龙,彭晓东,高辰,杨震.融合轨道动力学的小行星探测器自主视觉定位[J].哈尔滨工业大学学报,2024,56(5):19.DOI:10.11918/202202032 |
| FAN Mingrui,ZHANG Shidong,LI Yun,NIU Wenlong,PENG Xiaodong,GAO Chen,YANG Zhen.Autonomous visual localization for asteroid probe fusion orbital dynamics[J].Journal of Harbin Institute of Technology,2024,56(5):19.DOI:10.11918/202202032 |
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| 融合轨道动力学的小行星探测器自主视觉定位 |
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樊铭瑞1,2,3,张世栋1,2,3,李运1,2,牛文龙1,2,3,彭晓东1,2,高辰1,2,杨震1,2
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(1.中国科学院国家空间科学中心,北京 100190;2.中国科学院复杂航天系统电子信息技术重点实验室 (中国科学院国家空间科学中心),北京 100190;3.中国科学院大学,北京 100049)
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| 摘要: |
| 针对小行星探测器高精度自主视觉定位问题,提出了一种融合轨道动力学的深空探测器自主视觉定位方法,能修正视觉视觉定位与地图构建算法(simultaneous localization and mapping,SLAM)的定位误差。该方法通过融合轨道动力学的轨道改进技术,能够在缺乏表面先验信息、无人工手动标记的场景下,实现探测器的高精度视觉导航,并建立小行星表面稠密三维模型。首先,基于视觉同时定位和建图方法（VSLAM）提取小行星表面特征,通过因子图优化算法估计探测器位姿, 设计回环检测提高定位精度;其次,重构行星表面三维模型,实现基于多面体法的行星不规则引力场建模;最后,提出了一种基于轨道动力学的伪相对运动轨道优化算法,将其作为物理约束修正视觉定位累积误差,分析反演视觉初始定轨误差在轨道动力学中的传播过程,实现修正视觉定位累积误差,改善初始定位结果。仿真实验结果表明,融合轨道动力学可以有效提升小行星探测视觉定位的精度,从而实现高精度导航,为深空探测技术的未来发展提供参考借鉴。 |
| 关键词: 深空探测 自主导航 轨道动力学 视觉SLAM 状态估计 |
| DOI：10.11918/202202032 |
| 分类号:V448.2 |
| 文献标识码:A |
| 基金项目:中科院基础前沿科学研究计划(22E0223301)；中国科学院青年创新促进会(E1213A02) |
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| Autonomous visual localization for asteroid probe fusion orbital dynamics |
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FAN Mingrui1,2,3,ZHANG Shidong1,2,3,LI Yun1,2,NIU Wenlong1,2,3,PENG Xiaodong1,2,GAO Chen1,2,YANG Zhen1,2
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(1.National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China; 2.Key Laboratory of Electronics and Information Technology for Space Systems (National space Science Center, Chinese Academy of Sciences), Beijing 100190, China; 3.University of Chinese Academy of Sciences, Beijing 100049, China)
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| Abstract: |
| To address the problem of high-precision autonomous visual positioning of asteroid probes, this paper proposes an autonomous visual positioning method for deep space probes incorporating orbital dynamics, which can correct the positioning errors of the simultaneous localization and mapping (SLAM) algorithm. The method enables high-precision visual navigation of the probe and the creation of a dense 3D model of the asteroid surface in a scenario, where there is no prior information on the surface and no manual markers by incorporating orbital dynamics for orbit improvement. Firstly, the visual simultaneous localization and mapping method (VSLAM) is used to extract the asteroid surface features, estimate the probe attitude utilizing a factorisation algorithm, and design loopback detection to improve the localisation accuracy. Secondly, we reconstruct the 3D model of the surface of the planet and realise the irregular gravitational field modelling of the planet based on the polyhedral method. Finally, a pseudo-relative motion orbit optimisation algorithm based on orbital dynamics is proposed as a physical constraint to correct the accumulated visual positioning error, and the propagation process of the inverse visual initial orbiting error in orbital dynamics is analysed to correct the accumulated visual positioning error and improve the initial positioning results. The experimental simulation results show that the fused orbital dynamics can effectively improve the accuracy of visual positioning for asteroid detection, thus realising high-precision navigation and providing a reference for the future development of deep space exploration technology. |
| Key words: deep space exploration autonomous navigation orbital dynamics visual SLAM state estimation |
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