A Robust Regression Model for Simultaneous Localization and Mapping in Autonomous Mobile Robot

Authors:
Xinzheng Zhang;Ahmad B. Rad;Yiu-Kwong Wong
Affiliations:
Department of Electrical Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong;Simon Fraser University, School of Engineering Science, Mechatronics Systems Engineering, Surrey, Canada V3T 0A3;Department of Electrical Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong
Venue:
Journal of Intelligent and Robotic Systems
Year:
2008

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Abstract

Segment-based maps as sub-class of feature-based mapping have been widely applied in simultaneous localization and map building (SLAM) in autonomous mobile robots. In this paper, a robust regression model is proposed for segment extraction in static and dynamic environments. We adopt the MM-estimate to consider the noise of sensor data and the outliers that correspond to dynamic objects such as the people in motion. MM-estimates are interesting as they combine high efficiency and high breakdown point in a simple and intuitive way. Under the usual regularity conditions, including symmetric distribution of the errors, these estimates are strongly consistent and asymptotically normal. This robust regression technique is integrated with the extended Kalman filter (EKF) to build a consistent and globally accurate map. The EKF is used to estimate the pose of the robot and state of the segment feature. The underpinning experimental results that have been carried out in static and dynamic environments illustrate the performance of the proposed segment extraction method.