RGB-D Sensor-based Visual Target Detection and Tracking for an Intelligent Wheelchair Robot in Indoors Environments

The ability to follow or move alongside a person is a necessary skill for an autonomous mobile agent that works with human users. To accomplish such tasks, we develop a new scheme of visual-target detection and tracking for a wheelchair robot equipped with Microsoft Kinect that captures RGB images along with per-pixel depth information (RGB-D camera). The speeded-up semi-supervised online boosting algorithm is employed to provide the robust description of feature for environments and the target person from RGB images. Based on the environmental Haar-like features, we utilize an extended Kalman filter (EKF) based localization to estimate robot pose. Then obstacle avoidance navigation approach based on eta(3) spline trajectory planning and optimization are proposed for the wheelchair robot. Finally, the experimental results are provided to demonstrate the effectiveness and feasibility in real world environments.