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1127060从单个特征对应的n个视图中测量非完整地面车辆的运动估计0黄坤 上海科技大学0huangkun1@shanghaitech.edu.cn0Yifu Wang 澳大利亚国立大学0u5434194@anu.edu.au0Laurent Kneip上海科技大学0lkneip@shanghaitech.edu.cn0摘要0地面车辆的平面运动通常是非完整的,这使得可以从单点特征对应解决双视图相对姿态问题。然而,人工环境(如地下停车场)主要由线特征主导。受到平面三焦张量及其处理线特征的能力的启发,我们对非完整车辆的局部圆形运动建立了一个n线性约束,能够处理任意大且密集的视图窗口。我们证明,在局部恒定车速的假设下,这仍然是一个单变量问题,并且可以透明地处理点和垂直线对应。特别地,我们证明了应用Vi`ete公式来外推角度倍数的三角函数和Weierstrass代换将问题转化为仅寻找单变量多项式的根的问题。我们提出了这种新型求解器的完整理论,并在模拟和实际数据上进行了测试。我们的结果证明它成功处理了各种相关场景,最终优于1点两视图求解器。01. 引言0自动驾驶车辆承诺成为未来市场上的一项颠覆性技术。该主题目前在工业和学术界都得到了深入研究,任何成功的结果都严重依赖于可靠的在线解决方案来解决自定位和环境建图的相互依赖问题。虽然最强大的解决方案依赖于包括激光雷达和摄像头在内的多种传感器,但社区仍然对开发仅依赖视觉的替代方案保持高度兴趣。摄像头是经济实惠的即将上市的传感器,可能会打开更低成本的解决方案。0即使在没有其他外部感知传感器的情况下,自动驾驶车辆也可以在更受控制且不太关键的场景中实现更高级别的自主性[6]。虽然已经提出了直接稀疏[2]、半稠密[3]和稠密方法[26],但基于稀疏特征对应的定位和建图的传统方法仍然非常重要。除了出色的计算效率,它还为地点识别提供了有价值的信息,并能够无缝集成到全局优化目标中[25]。一个特别有趣的案例是使用单个前视摄像头进行相对姿态估计的问题,因为这样的传感器已经可以在市场上的车辆上找到。本文重点研究了在汽车上安装的单个摄像头的相对姿态估计的相关基本问题。在初始化阶段,需要解决这个问题,此时对车辆的运动或环境都没有先验信息。此外,顺序应用还能够直接解决视觉里程计问题[28],从而实现车辆的在线相对自定位。使用单个摄像头的经典解决方案至少需要五个点特征对应。然而,正如[31]所示,地面车辆的非完整运动自由度较少,从而减少了所需的对应数量。地面车辆的运动可以近似为阿克曼模型,该模型将局部轨迹强制为平面上的圆弧,并且车辆的航向保持与圆弧切线相切。该模型仅依赖于两个参数,即圆的半径和弧的内角。此外,仅使用单个摄像头使得尺度不可观测,后者仅影响圆的半径。正如[31]中所概述的,这一事实允许仅从单个特征对应中解决相对旋转角度。[31]中提出的1点求解器的缺点是...θAAAB7XicbZC7SgNBFIbPeo3rLWppsxgEq7Bro40YtLGMYC6QLGF2MpuMmZ1ZZs4KYQn4CDYWitj6KPZ2vo2TS6GJPwx8/P85zDknSgU36PvfztLyyuraemHD3dza3tkt7u3Xjco0ZTWqhNLNiBgmuGQ15ChYM9WMJJFgjWhwPc4bD0wbruQdDlMWJqQnecwpQWvV29hnSDrFkl/2J/IWIZhB6fLTvXgEgGqn+NXuKpolTCIVxJhW4KcY5kQjp4KN3HZmWErogPRYy6IkCTNhPpl25B1bp+vFStsn0Zu4vztykhgzTCJbmRDsm/lsbP6XtTKMz8OcyzRDJun0ozgTHipvvLrX5ZpRFEMLhGpuZ/Von2hC0R7ItUcI5ldehPppObB8G5QqVzBVAQ7hCE4ggDOowA1UoQYU7uEJXuDVUc6z8+a8T0uXnFnPAfyR8/EDElCQ8g== 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