VERIFICATION OF GRID BASED FASTSLAM WITH MULTIPLE CANDIDATES OF PARTICLES

Reo Yasuda, H. Oya, Y. Hoshi
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Abstract

In this paper, we propose a strategy for improving the accuracy of own positions in FastSLAM (Fast-Simultaneous Localization And Mapping). One can see that estimation of self-position and mapping are important for an autonomous mobile robot. As one approach to do this, GPS (Global Positioning System) based self-positioning methods have been proposed. However, GPS-based method has disadvantage that positioning is difficult in the environment where radio waves cannot be received. On the other hand, it is well known that SLAM can perform self-positioning by sensing the external environment based on LiDAR sensors, cameras and other devices. Since SLAM does not require online communication, estimate of self-position and mapping can be performed without being affected by the driving environment. FastSLAM is one of the SLAM algorithms, and samples the path using a particle filter. Each particle represents one possible motion path of robots in the particle filter, and the observed information is used to calculate the weight of each particle and evaluate each path. In FastSLAM, when self-location is determined, a particle is selected by comparing its likelihood as evaluated by the likelihood function. Since this depends on observed information, the reliability of the estimate depends on the accuracy of the sensor. Thus, there is possibility that there are more useful particles among those not selected. In this paper, we propose a new FastSLAM algorithm that selects multiple candidates of particles in FastSLAM algorithm. In the proposed approach, multiple candidates are stored and estimates of self-position is determined by using multiple candidates in the next step. By maintaining multiple particles, more robust self-position estimation and mapping can be performed comparing with the conventional FastSLAM. In this paper, we show the proposed algorithm and numerical simulations are shown to evaluate the performance of the proposed algorithm.
基于网格的多候选粒子快速拉伸验证
在本文中,我们提出了一种在 FastSLAM(快速同步定位与绘图)中提高自身位置精度的策略。可以看出,对自主移动机器人来说,估计自身位置和绘制地图非常重要。作为实现这一目标的方法之一,人们提出了基于 GPS(全球定位系统)的自我定位方法。然而,基于 GPS 的方法有一个缺点,即在无法接收无线电波的环境中很难定位。另一方面,众所周知,SLAM 可以通过基于激光雷达传感器、摄像头和其他设备的外部环境感知来进行自我定位。由于 SLAM 不需要在线通信,因此可以在不受行驶环境影响的情况下进行自我定位估算和绘图。FastSLAM 是 SLAM 算法之一,使用粒子滤波器对路径进行采样。在粒子滤波器中,每个粒子代表机器人的一条可能运动路径,观察到的信息用于计算每个粒子的权重和评估每条路径。在 FastSLAM 中,当自我定位确定后,通过比较似然函数评估的似然值来选择粒子。由于这取决于观测到的信息,因此估计值的可靠性取决于传感器的精度。因此,在未被选中的粒子中可能存在更多有用的粒子。本文提出了一种新的 FastSLAM 算法,即在 FastSLAM 算法中选择多个候选粒子。在所提出的方法中,多个候选粒子被存储起来,并在下一步中通过使用多个候选粒子来确定自身位置的估计值。与传统的 FastSLAM 相比,通过保留多个粒子,可以执行更稳健的自位置估计和映射。在本文中,我们展示了所提出的算法,并通过数值模拟来评估所提出算法的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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