A Monte Carlo Algorithm for Multi-Robot Localization

Abstract

This paper presents a statistical algorithm for collaborative mobile robot localization. Our approach uses a sample-based version of Markov localization, capable of localizing mobile robots in an any-time fashion. When teams of robots localize themselves in the same environment, probabilistic methods are employed to synchronize each robot's belief whenever one robot detects another. As a result, the robots localize themselves faster, maintain higher accuracy, and high-cost sensors are amortized across multiple robot platforms. The paper also describes experimental results obtained using two mobile robots, using computer vision and laser range finding for detecting each other and estimating each other's relative location. The results, obtained in an indoor office environment, illustrate drastic improvements in localization speed and accuracy when compared to conventional single-robot localization.

Cooperative Localization in Wireless Networks

“…Bayesian approaches to localization have been used in robotics for the noncooperative mobile single-agent case [18] and for the cooperative mobile multiagent case [49], [60], [107]. Bayesian cooperative localization for networks without mobility was investigated in [14], [108], and [109].…”

Map representations for mobile robot localization

“…This probabilistic representation allows for a more flexible representation of environmental uncertainty. Therefore, occupancy grid maps are commonly employed in probabilistic localization for mobile robots, such as Kalman Filter localization [19], Markov localization [20] and Monte Carlo Localization [21],and also widely used in SLAM. For example, Wijaya et al [22] proposed an occupancy grid map mapping method on Hector SLAM technique, which could map the surrounding environment accurately using only lidar, as shown in Figure 4.…”

Optimal fusion‐based localization method for tracking of smartphone user in tall complex buildings

“…The leading indicators of a phone's limited resources are its limited processing power, battery life, and storage space. As smartphone computing capability increases, complex fusion positioning techniques like simultaneous localization and mapping and Monte Carlo localization of mobile robots may eventually enter the field of smartphone‐based indoor localization [41, 42]. Fusion placement, however, can currently only use a tiny percentage of the CPU's processing time to maintain the system's smooth operation.…”