Abstract
This paper presents a sensor fusion strategy applied for Simultaneous Localization and Mapping (SLAM) in dynamic environments. The designed approach consists of two features: (i) the first one is a fusion module which synthesizes line segments obtained from laser rangefinder and line features extracted from monocular camera. This policy eliminates any pseudo segments that appear from any momentary pause of dynamic objects in laser data. (ii) The second characteristic is a modified multi-sensor point estimation fusion SLAM (MPEF-SLAM) that incorporates two individual Extended Kalman Filter (EKF) based SLAM algorithms: monocular and laser SLAM. The error of the localization in fused SLAM is reduced compared with those of individual SLAM. Additionally, a new data association technique based on the homography transformation matrix is developed for monocular SLAM. This data association method relaxes the pleonastic computation. The experimental results validate the performance of the proposed sensor fusion and data association method.
Highlights
A crucial characteristic of an autonomous mobile robot is its ability to determine its whereabouts and make sense of its static and dynamic environments
We suggest a sensor fusion strategy including feature fusion and modified MPEF-Simultaneous Localization and Mapping (SLAM)
Our feature fusion policy incorporates the line features extracted by a monocular camera with the segments represented by robust regression model from a laser sensor, the purpose of which is to remove the potential pseudo laser segments corresponding to the moving objects
Summary
A crucial characteristic of an autonomous mobile robot is its ability to determine its whereabouts and make sense of its static and dynamic environments. We present a sensor fusion strategy for line-based SLAM applied in dynamic environments This approach fuses the sensor information obtained from a monocular camera and laser rangefinder and includes two modules. This modified MPEF-SLAM fuses the state variable and its covariance estimated from individual SLAM procedure and propagates fused values backward to each SLAM process to reduce the error of robot pose and line feature positions. Another advantage of the modified MPEF-SLAM is that its implementation is on the basis of two parallel running SLAM processes, which can avoid unexpected events.
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