Abstract
Precise localization of mobile robots in uncertain environments is a fundamental and crucial issue in robotics. In this paper, to deal with the unbounded accumulated errors of dead reckoning (DR)-based localization, wireless sensor network (WSN)-based localization is applied to calibrate the uncertainty of odometry using a Kalman filter (KF). In addition, to further aid in obtaining precise positions and reduce uncertainty, a novel backward dead reckoning (BDR) localization approach is proposed. The experimental results demonstrate the success and reliability of the proposed method.
Highlights
A fundamental problem for an autonomous mobile robot is knowing its current position and orientation by sensorial observation and previous accurate localization and it is a popular research topic in the mobile robot community [1]
The problems studied in this paper focus on how a mobile robot can obtain its current location and its previous trajectory precisely, in both of these two typical kinds of indoor environments with a known starting point. 2.2 dead reckoning (DR)‐based Localization As shown in Figure 3, a differential‐drive mobile robot is used in our research
The previous estimation deduced by the back recurrence equations with the posterior estimation can be considered as a new measurement by a third sensor, it can be fused with the optimal estimation given by the Kalman filter using the localization information of the odometry and wireless sensor network (WSN) in this position, as calculated by shown in Figure x 1,k
Summary
A fundamental problem for an autonomous mobile robot is knowing its current position and orientation by sensorial observation and previous accurate localization and it is a popular research topic in the mobile robot community [1]. These methods are capable of reducing the uncertainties of position estimation, the landmarks generally require a regular shape or other regular characteristics, which is difficult in practical applications. The problems studied in this paper focus on how a mobile robot can obtain its current location and its previous trajectory precisely, in both of these two typical kinds of indoor environments with a known starting point. The section will present an effective approach to combining the WSN and DR‐based localization for mobile robots
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