A predictive object detection algorithm was developed to investigate the practicality of using advanced filtering on
stereo vision object detection algorithms such as the X-H Map. Obstacle detection with stereo vision is inherently
noisy and non linear. This paper describes the X-H Map algorithm and details a method of improving the accuracy
with the Unscented Kalman Filter (UKF). The significance of this work is that it details a method of stereo vision
object detection and concludes that the UKF is a relevant method of filtering that improves the robustness of obstacle
detection given noisy inputs. This method of integrating the UKF for use in stereo vision is suitable for any standard
stereo vision algorithm that is based on pixel matching (stereo correspondence) from disparity maps.
Three dimensional visual recognition and measurement are important in many machine vision applications. In some cases, a stationary camera base is used and a three-dimensional model will permit the measurement of depth information from a scene. One important special case is stereo vision for human visualization or measurements. In cases in which the camera base is also in motion, a seven dimensional model may be used. Such is the case for navigation of an autonomous mobile robot. The purpose of this paper is to provide a computational view and introduction of three methods to three-dimensional vision. Models are presented for each situation and example computations and images are presented. The significance of this work is that it shows that various methods based on three-dimensional vision may be used for solving two and three dimensional vision problems. We hope this work will be slightly iconoclastic but also inspirational by encouraging further research in optical engineering.
This paper presents a novel, simple and fast algorithm to produce a "floor plan" obstacle map in real time using video. The XH-map algorithm is a transformation of stereo vision data in disparity map space into a two dimensional obstacle map space using a method that can be likened to a histogram reduction of image information. The classic floor-ground background noise problem is addressed with a simple one-time semi-automatic calibration method incorporated into the algorithm. This implementation of this algorithm utilizes the Intel Performance Primitives library and OpenCV libraries for extremely fast and efficient execution, creating a scaled obstacle map from a 480x640x256 stereo pair in 1.4 milliseconds. This algorithm has many applications in robotics and computer vision including enabling an "Intelligent Robot" robot to "see" for path planning and obstacle avoidance.
The Bearcat “Cub” Robot is an interactive, intelligent, Autonomous Guided Vehicle (AGV) designed to serve in unstructured environments. Recent advances in computer stereo vision algorithms that produce quality disparity and the availability of low cost high speed camera systems have simplified many of tasks associated with robot navigation and obstacle avoidance using stereo vision. Leveraging these benefits, this paper describes a novel method for autonomous navigation and obstacle avoidance currently being implemented on the UC Bearcat Robot. The core of this approach is the synthesis of multiple sources of real-time data including stereo image disparity maps, tilt sensor data, and LADAR data with standard contour, edge, color, and line detection methods to provide robust and intelligent obstacle avoidance. An algorithm is presented with Matlab code to process the disparity maps to rapidly produce obstacle size and location information in a simple format, and features cancellation of noise and correction for pitch and roll. The vision and control computers are clustered with the Parallel Virtual Machine (PVM) software. The significance of this work is in presenting the methods needed for real time navigation and obstacle avoidance for intelligent autonomous robots.