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首页> 外文期刊>IEEE transactions on industrial informatics >Fast and Robust Data Association Using Posterior Based Approximate Joint Compatibility Test
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Fast and Robust Data Association Using Posterior Based Approximate Joint Compatibility Test

机译：使用基于后验的近似联合兼容性测试进行快速而稳健的数据关联

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摘要

Data association is a fundamental problem in multisensor fusion, tracking, and localization. The joint compatibility test is commonly regarded as the true solution to the problem. However, traditional joint compatibility tests are computationally expensive, are sensitive to linearization errors, and require the knowledge of the full covariance matrix of state variables. The paper proposes a posterior-based joint compatibility test scheme to conquer the three problems mentioned above. The posterior-based test naturally separates the test of state variables from the test of observations. Therefore, through the introduction of the robot movement and proper approximation, the joint test process is sequentialized to the sum of individual tests; therefore, the test has $O(n)$ complexity (compared with $O(n^{2})$ for traditional tests), where $n$ denotes the total number of related observations. At the same time, the sequentialized test neither requires the knowledge to the full covariance matrix of state variables nor is sensitive to linearization errors caused by poor pose estimates. The paper also shows how to apply the proposed method to various simultaneous localization and mapping (SLAM) algorithms. Theoretical analysis and experiments on both simulated data and popular datasets show the proposed method outperforms some classical algorithms, including sequential compatibility nearest neighbor (SCNN), random sample consensus (RANSAC), and joint compatibility branch and bound (JCBB), on precision, efficiency, and robustness.
机译：数据关联是多传感器融合，跟踪和定位中的一个基本问题。联合兼容性测试通常被认为是该问题的真正解决方案。但是，传统的联合兼容性测试计算量大，对线性化误差敏感，并且需要了解状态变量的完整协方差矩阵。本文提出了一种基于后验的联合兼容性测试方案，以克服上述三个问题。基于后验的检验自然会将状态变量的检验与观察值的检验分开。因此，通过引入机器人运动和适当的近似，将联合测试过程按顺序进行到各个测试的总和。因此，测试具有$ O（n）$的复杂度（与传统测试的$ O（n ^ {2}）$相比），其中$ n $表示相关观察的总数。同时，顺序测试既不需要了解状态变量的完整协方差矩阵，也不会对不良姿势估计所导致的线性化误差敏感。本文还展示了如何将所提出的方法应用于各种同时定位和映射（SLAM）算法。对模拟数据和流行数据集进行的理论分析和实验表明，该方法在精度，效率，性能方面均优于一些经典算法，包括顺序兼容最近邻（SCNN），随机样本共识（RANSAC）和联合兼容分支定界（JCBB）。和健壮性。

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