Distributed Multi - Robot Rao - Blac k wellized Particle Filt er ing Simultaneous Localization and Mapping with Consensus Calculation of Particle Weight and Posterior Parameters

Abstract

Extensive research on single-robot simultaneous localization and mapping (SLAM) over the past decade has provided good estimation results in mapping small environment. This raises the idea for building a larger map by using some robots that are assembled within the team, also called multi-robot SLAM. There are two types of multirobot SLAM, that is, centralized and distributed multi-robot SLAM. Centralized multirobot SLAM method still has problems in case of system failure on some robots, especially the system failure on the robot central. Distributed multi-robot SLAM was developed to overcome the weaknesses in centralized multi-robot SLAM by: 1) building a map of larger environment by fusing local maps from a group of robots, 2) eliminating the dependence on central processing by using distributed computing, 3) overcoming the vulnerability in case of system failure in some robots, especially the system failure in the robot central, and 4) eliminating centralized communication, in which each robot requires only local communication with its neighboring robots. However, the distributed multirobot SLAM method has not yielded good results on map estimates and localization estimates. This paper proposes consensus-based distributed multi-robot SLAM method that can be applied to map common environments. We use FastSLAM algorithm as the main base in developing distributed multi-robot SLAM. We suggest using one of two selection of consensus parameters, that is, particle weight and posterior parameter. We assume that each robot has the same motion model and observation model; therefore, the system noise and observation noise is the same. The aim of this paper is to design consensus based distributed multi-robot SLAM method that provides better map estimation and localization results when applied to common environments. We concentrate to test the proposed methods on aspects: 1) root mean square error (RMSE) of map to see the map estimation performance, 2) RMSE of localization to see the robot's pose estimation performance, 3) mapping coverage to see how long it will take until all features are observed, and 4) processing time to see how long the computation process takes place per timestep.

Mean-shift Object Tracking Algorithm with Systematic Sampling Technique

“…Object tracking is developed to recognize objects and estimate their position at each time change. The existence of object tracking is closely related to other fields, such as distance learning [1], [2], surveillance [3]- [8], robotic navigation [9], vehicle localization [10], [11], and smart cities [3]. One of the problems in object tracking algorithms include the need to ensure that they follow objects in real time and that their resistance to changes in environmental conditions, such as lighting, is increased.…”