A Feasible Tracking Method of Augmented Reality for Supporting Fieldwork of Nuclear Power Plant
VMR '09 Proceedings of the 3rd International Conference on Virtual and Mixed Reality: Held as Part of HCI International 2009
SLAM in large indoor environments with low-cost, noisy, and sparse sonars
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Sonar based simultaneous localization and mapping using a neuro evolutionary optimization
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Measurement noise estimator assisted extended kalman filter for SLAM problem
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
VPass: algorithmic compass using vanishing points in indoor environments
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
A hybrid approach to RBPF based SLAM with grid mapping enhanced by line matching
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
EKF-Based Localization of a Wheeled Mobile Robot in Structured Environments
Journal of Intelligent and Robotic Systems
Integrated PSO and line based representation approach for SLAM
Proceedings of the 2011 ACM Symposium on Applied Computing
Improving odometry using a controlled point laser
Autonomous Robots
On the way to a real-time on-board orthogonal SLAM for an indoor UAV
ICIRA'11 Proceedings of the 4th international conference on Intelligent Robotics and Applications - Volume Part I
Robotics and Autonomous Systems
A real-time on-board orthogonal SLAM for an indoor UAV
ICIRA'12 Proceedings of the 5th international conference on Intelligent Robotics and Applications - Volume Part III
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This paper describes a geometrically constrained Extended Kalman Filter (EKF) framework for a line feature based SLAM, which is applicable to a rectangular indoor environment. Its focus is on how to handle sparse and noisy sensor data, such as PSD infrared sensors with limited range and limited number, in order to develop a low-cost navigation system. It has been applied to a vacuum cleaning robot in our research. In order to meet the real-time objective with low computing power, we develop an efficient line feature extraction algorithm based upon an iterative end point fit (IEPF) technique assisted by our constrained version of the Hough transform. It uses a geometric constraint that every line is orthogonal or parallel to each other because in a general indoor setting, most furniture and walls satisfy this constraint. By adding this constraint to the measurement model of EKF, we build a geometrically constrained EKF framework which can estimate line feature positions more accurately as well as allow their covariance matrices to converge more rapidly when compared to the case of an unconstrained EKF. The experimental results demonstrate the accuracy and robustness to the presence of sensor noise and errors in an actual indoor environment.