无数据关联的基于路标的SLAM
Data-Association-Free Landmark-based SLAM
AI总结：
针对数据关联未知的基于路标的SLAM问题，提出将其拆分为内部估计问题和外部路标数量估计问题的方法，建立了与离散-连续优化技术的新联系，实验表明其性能优于典型基线且可比肩预言机基线。
摘要：
我们研究了数据关联未知情况下的基于路标的SLAM（同时定位与地图构建）：机器人在完全未知的环境中导航，必须同时推断自身轨迹、环境中数量未知的路标的位置，以及测量值与路标之间潜在的数据关联。这一设定颇具研究价值，原因在于：（i）它出现在从数据关联故障中恢复，或使用信息贫乏传感器进行SLAM的场景中；（ii）它揭示了无论前端数据关联方法如何，基于路标的SLAM问题的基本极限（及难度）；（iii）它推广了现有假设数据关联已知或部分已知的方法。我们将该问题拆分为估计轨迹、路标位置和数据关联的内部问题，以及估计路标数量的外部问题来求解。我们的方法与离散-连续优化（例如k-means聚类）领域的现有技术建立了有用且新颖的联系，有望催生新的研究。我们在大量仿真和真实数据集上验证了所提出的方法，结果表明，所提技术优于典型的数据关联基线，甚至能与可获知路标数量和每个路标初始猜测的“预言机”基线性能相当。
英文摘要：
We study landmark-based SLAM with unknown data association: our robot navigates in a completely unknown environment and has to simultaneously reason over its own trajectory, the positions of an unknown number of landmarks in the environment, and potential data associations between measurements and landmarks. This setup is interesting since: (i) it arises when recovering from data association failures or from SLAM with information-poor sensors, (ii) it sheds light on fundamental limits (and hardness) of landmark-based SLAM problems irrespective of the front-end data association method, and (iii) it generalizes existing approaches where data association is assumed to be known or partially known. We approach the problem by splitting it into an inner problem of estimating the trajectory, landmark positions and data associations and an outer problem of estimating the number of landmarks. Our approach creates useful and novel connections with existing techniques from discrete-continuous optimization (e.g., k-means clustering), which has the potential to trigger novel research. We demonstrate the proposed approaches in extensive simulations and on real datasets and show that the proposed techniques outperform typical data association baselines and are even competitive against an "oracle" baseline which has access to the number of landmarks and an initial guess for each landmark.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
