Nonlinear Progressive Filtering for SE(2) Estimation
Nonlinear Progressive Filtering for SE(2) Estimation
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DOI：
10.23919/icif.2018.8455231
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发表时间：
2018-07
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影响因子：
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通讯作者：
Kailai Li;G. Kurz;Lukas Bernreiter;U. Hanebeck
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文献类型：
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作者：
Kailai Li;G. Kurz;Lukas Bernreiter;U. Hanebeck
本文提出了一种新的非线性渐进滤波方法，用于估计单位对偶四元数表示的SE（2）态。与以前发表的方法不同，测量模型不再需要假设为恒等式。我们的解决方案利用了类似宾厄姆的概率分布的确定性采样，该分布已被调整为同时对方向和平移进行建模。在测量更新步骤期间，估计值逐渐更新。我们的方法固有地结合了S E（2）的非线性结构，并实现了灵活的测量更新步骤。我们还给出了一个评估平面刚体运动估计的案例研究，是接近现实世界的场景。
In this paper, we present a novel nonlinear progressive filtering approach for estimating S E (2) states represented by unit dual quaternions. Unlike previously published approaches, the measurement model no longer needs to be assumed as identity. Our solution utilizes deterministic sampling on a Bingham-like probability distribution, which has been adapted to simultaneously model orientation and translation. During the measurement update step, the estimate gets progressively updated. Our approach inherently incorporates the nonlinear structure of S E (2) and enables a flexible measurement update step. We also give an evaluation for planar rigid body motion estimation with a case study that is close to real-world scenarios.