Simultaneous localization and mapping (SLAM) is a well-known and fundamental topic for autonomous robot navigation. Existing solutions include the FastSLAM family-based approaches which are based on Rao–Blackwellized particle filter. The FastSLAM methods slow down greatly when the number of landmarks becomes large. Furthermore, the FastSLAM methods use a fixed number of particles, which may result in either not enough algorithms to find a solution in complex domains or too many particles and hence wasted computation for simple domains. These issues result in reduced performance of the FastSLAM algorithms, especially on embedded devices with limited computational capabilities, such as commonly used on mobile robots. To ease the computational burden, this paper proposes a modified version of FastSLAM called Adaptive Computation SLAM (ACSLAM), where particles are predicted only by odometry readings, and are updated only when an expected measurement has a maximum likelihood. As for the states of landmarks, they are also updated by the maximum likelihood. Furthermore, ACSLAM uses the effective sample size (ESS) to adapt the number of particles for the next generation. Experimental results demonstrated that the proposed ACSLAM performed 40% faster than FastSLAM 2.0 and also has higher accuracy.

中文翻译：

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结合探索和路径规划策略的自适应计算 SLAM

同时定位和建图（SLAM）是自主机器人导航的一个众所周知的基础主题。现有的解决方案包括基于 Rao-Blackwellized 粒子滤波器的 FastSLAM 系列方法。当地标数量变大时，FastSLAM 方法的速度会大大降低。此外，FastSLAM 方法使用固定数量的粒子，这可能导致算法不足，无法在复杂域中找到解决方案，或者粒子太多，从而浪费了简单域的计算。这些问题导致 FastSLAM 算法的性能下降，尤其是在计算能力有限的嵌入式设备上，例如移动机器人上常用的。为了减轻计算负担，本文提出了一种改进版本的 FastSLAM，称为自适应计算 SLAM (ACSLAM)，其中粒子仅通过里程计读数预测，并且仅在预期测量具有最大似然时更新。至于地标的状态，它们也被最大似然更新。此外，ACSLAM 使用有效样本大小 (ESS) 来调整下一代粒子的数量。实验结果表明，所提出的 ACSLAM 的性能比 FastSLAM 2.0 快 40%，并且具有更高的准确度。