外观
论文arXiv 2401.058362 个月前
多传感器融合导航中的状态估计：优化与滤波
原文链接：On state estimation in multi-sensor fusion navigation: optimization and filtering（arXiv 2401.05836 · 发布日期：2026-08-03）
On state estimation in multi-sensor fusion navigation: optimization and filtering
本文分析多传感器融合导航的状态估计方法，并在蒙特卡洛仿真和基于VO的车载消融实验中验证等价性。
核心要点
- 导航为上层应用提供位置与姿态，多传感器融合任务可归约为状态估计问题。
- 比较优化与滤波的MLE框架，分析相同线性化点、观测模型、测量和高斯噪声下的等价性。
- 指出实时策略差异会破坏理论等价；调整滤波策略后，其与优化严格等同。
- 建议未来传感器融合研究关注具体算法和策略，而非状态估计途径本身。
验证范围与局限：验证范围为蒙特卡洛仿真和基于视觉里程计的车载消融实验。
英文摘要 (Abstract)
The essential of navigation, perception, and decision-making which are basic tasks for intelligent robots, is to estimate necessary system states. Among them, navigation is fundamental for other upper applications, providing precise position and orientation, by integrating measurements from multiple sensors. With observations of each sensor appropriately modelled, multi-sensor fusion tasks for navigation are reduced to the state estimation problem which can be solved by two approaches: optimization and filtering. Recent research has shown that optimization-based frameworks outperform filtering-based ones in terms of accuracy. However, both methods are based on maximum likelihood estimation (MLE) and should be theoretically equivalent with the same linearization points, observation model, measurements, and Gaussian noise assumption. In this paper, we deeply dig into the theories and existing strategies utilized in both optimization-based and filtering-based approaches. It is demonstrated that the two methods are equal theoretically, but this equivalence corrupts due to different strategies applied in real-time operation. By adjusting existing strategies of the filtering-based approaches, the Monte-Carlo simulation and vehicular ablation experiments based on visual odometry (VO) indicate that the strategy adjusted filtering strictly equals to optimization. Therefore, future research on sensor-fusion problems should concentrate on their own algorithms and strategies rather than state estimation approaches.
作者：Feng Zhu, Zhuo Xu, Xveqing Zhang, Weijie Chen, Xiaohong Zhang, Mohamed Freeshah
来源
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