Graph-SLAM is a well-established algorithm for constructing a topological map of the environment while simultaneously attempting the localisation of the robot. It relies on scan matching algorithms to align noisy observations along robot's movements to compute an estimate of the current robot's location. We propose a fundamentally different approach to scan matching tasks to improve the estimation of roto-translation displacements and therefore the performance of the full SLAM algorithm. A Monte-Carlo approach is used to generate weighted hypotheses of the geometrical displacement between two scans, and then we cluster these hypotheses to compute the displacement that results in the best alignment. To cope with clusterization on roto-translations, we propose a novel clustering approach that robustly extends Gaussian Mean-Shift to orientations by factorizing the kernel density over the roto-translation components. We demonstrate the effectiveness of our method in an extensive set of experiments using both synthetic data and the Intel Research Lab's benchmarking datasets. The results confirms that our approach has superior performance in terms of matching accuracy and runtime computation than the state-of-the-art iterative point-based scan matching algorithms.
翻译:图形- SLAM 是构建环境地形图的既定算法, 同时试图将机器人本地化。 它依赖于扫描匹配算法, 将机器人运动中的噪音观测与机器人当前位置的估计数相匹配。 我们提出一种根本不同的方法来扫描匹配任务, 以改进对旋转变换偏移的估计, 从而改善整个 SLAM 算法的性能 。 Monte- Carlo 方法被用来生成两个扫描之间几何偏移的加权假设, 然后我们将这些假设组合在一起, 以计算出最佳对齐结果的偏移 。 为了应对旋转变换上的群集化, 我们提出了一种新的组合法, 通过将旋转变换成组件的内核密度系数化, 将高斯中值- 希夫 到方向上。 我们用合成数据和 Intel 研究实验室的基准数据集来展示我们的方法在一系列广泛的实验中的有效性 。 结果证实, 我们的方法在匹配精确性和运行时间的计算方面表现优于状态的基点对比扫描算法。