Vision-based navigation system for obstacle avoidance in complex environments

We present a mobile system capable of autonomous navigation through complex unknown environments that contain stationary obstacles and moving targets. The intelligent system is composed of several fine-tuned computer vision algorithms running onboard in real-time. The first of these utilizes onboard cameras to allow for stereoscopic estimation of depths within the surrounding environment. The novelty of the approach lies in algorithmic efficiency and the ability of the system to complete a given task through the utilization of scene reconstruction and in making real-time automated decisions. Secondly, the system performs human body detection and recognition using advanced local binary pattern (LBP) descriptors. The LBP descriptors allow the system to perform human identification and tracking tasks irrespective of lighting conditions. Lastly, face detection and recognition allow for an additional layer of biometrics to ensure the correct target is being tracked. The face detection algorithm utilizes the Voila-Jones cascades, which are combined to create a pose invariant face detection system. Furthermore, we utilize a modular principal component analysis technique to perform pose-invariant face recognition. In this paper, we present the results of a series of experiments designed to automate the security patrol process. Our mobile security system completes a series of tasks within varying scenarios that range in difficulty. The tasks consist of tracking an object in an open environment, following a person of interest through a crowded environment, and following a person who disappears around a corner.