基于凯利地图的证实最优的旋转和姿势估计.
Timothy D Barfoot1, Connor Holmes1, Frederike Dümbgen1
1Robotics Institute, University of Toronto, Toronto, ON, Canada.
概括
我们开发了用于旋转和姿势估计的新凸松,即使在噪音下,也保证了整体的最佳性. 这种方法使用半确定的程序 (SDPs) 准确的轨迹和占平均值.
科学领域:
- 机器人和计算机视觉 机器人和计算机视觉
- 优化和数学编程的优化
背景情况:
- 现有的旋转和姿势估计方法通常假定特定的噪声模型,如矩阵·米塞斯-菲舍尔分布.
- 李代数中的 anisotropic 噪声是旋转不确定性的常见替代表示.
研究的目的:
- 为旋转引入新的凸宽松,并提出估计问题.
- 为这些估计任务提供后续的总体最佳性保证.
- 为了解决基本的平均和复杂的轨迹估计问题.
主要方法:
- 基于使用凯利地图的噪声模型制定估计问题.
- 将这些问题转换为二次制约的二次程序 (QCQPs).
- 将QCQP放松为半确定的程序 (SDP),可以通过内部点方法解决,利用拉格朗的强二元性.
主要成果:
- 成功制定SDP放松用于旋转平均,姿势平均和轨迹估计.
- 证明拟议的SDP宽松措施的实际适用性和有效性.
- 在SDP配方中识别和处理冗余约束.
结论:
- 拟议的凸放松提供了对旋转和姿势估计的强有力的方法.
- 这些方法在实际噪音条件下提供了总体最佳性的后续保证.
- 这项工作扩大了用于解决复杂估计问题的工具包,以保证最佳解决方案.
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