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Simultaneous localization and mapping for tracked wheel robots combining monocular and stereo vision

Jesus F., Ventura R.

Journal of Automation Mobile Robotics and Intelligent Systems

2013

Vol. 7, No. 1

21--27

This paper addresses an online 6D SLAM method for a tracked wheel robot in an unknown and unstructured environment. While the robot pose is represented by its position and orientation over a 3D space, the environment is mapped with natural landmarks in the same space, autonomously collected using visual data from feature detectors. The observation model employs opportunistically features detected from either monocular and stereo vision. These features are represented using an inverse depth parametrization. The motion model uses odometry readings from motor encoders and orientation changes measured with an IMU. A dimensional-bounded EKF (DBEKF) is introduced here, that keeps the dimension of the state bounded. A new landmark classifier using a Temporal Difference Learning methodology is used to identify undesired landmarks from the state. By forcing an upper bound to the number of landmarks in the EKF state, the computational complexity is reduced to up to a constant while not compromising its integrity. All experimental work was done using real data from RAPOSA-NG, a tracked wheel robot developed for Search and Rescue missions.