Conference Paper

Robust sonar feature detection for the SLAM of mobile robot

Robotics & Bio-Mechatronics Laboratory, Pohang University of Science and Technology, Geijitsu, Gyeongsangbuk-do, South Korea
DOI: 10.1109/IROS.2005.1545284 Conference: Intelligent Robots and Systems, 2005. (IROS 2005). 2005 IEEE/RSJ International Conference on
Source: IEEE Xplore


Sonar sensor is an attractive tool for the SLAM of mobile robot because of their economic aspects. This cheap sensor gives relatively accurate range readings if disregarding angular uncertainty and specular reflections. However, these defects make feature detection difficult for the most part of the SLAM. This paper proposes a robust sonar feature detection algorithm. This algorithm gives feature detection methods for both point features and line features. The point feature detection method is based on the TBF (Wijk and Christensen, 2000) scheme. Moreover, three additional processes improve the performance of feature detection as follows; 1) stable intersections; 2) efficient sliding window update; and 3) removal of the false point features on the wall. The line feature detection method is based on the basic property of adjacent sonar sensors. Along the line feature, three adjacent sonar sensors give similar range readings. Using this sensor property, we propose a novel algorithm for line feature detection, which is simple and the feature can be obtained by using only current sensor data. The proposed feature detection algorithm gives a good solution for the SLAM of mobile robots because it gives an accurate feature information for both the point and line features even with sensor errors. Furthermore, a sufficient number of features are available to correct mobile robot pose. Experimental results of the EKF-based SLAM demonstrate the performance of the proposed feature detection algorithm in a home-like environment.

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    • "Odometry is playing an important role in both systems to estimate planar motion of the robot, though the hierarchical SLAM is less affected by odometry than the standard EKF-SLAM. The proposed method has an advantage over the previous works which only use either sonar features (Choi et al. 2005) or visual objects (Ahn et al. 2006), especially, in a large environment. Sonar-only SLAM works well in a small environment or local parts of a large environment. "
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    • "In this section, sonar feature detection for point feature and line feature is briefly described. More details can be found in [6] "
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    ABSTRACT: To implement an autonomous mobile robot, both SLAM and task based navigation algorithms should be performed successfully. Especially, the performance of the estimation while the mobile robot performs task based navigation should be guaranteed. For this purpose, we integrate a SLAM method and a navigation algorithm for practical autonomous mobile robot. The SLAM method combines sonar sensors and stereo camera together using the EKF-based SLAM. Fusing sonar features and visual objects can give correct data association via object recognition and high frequency update via sonar features. The navigation algorithm consists of global and local path planner when the goal position is given. The global path planner uses modified A* algorithm and it gives the mobile robot enough opportunity to detect the registered landmarks during moving to the goal position. As a local path planner, for safe obstacle avoidance, we propose circle following (CF) algorithm. The performance of the proposed algorithm was verified by experiments in home environment with dynamic obstacles
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