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This paper presents a methodology of using Kalman filter to minimize the error in stereo vision based distance measurement data (3D position of pedestrians). In stereo vision, little point mis-correspondence leads to a very bad estimate of depth during triangulation. There are robust correspondence algorithms but all of them suffer from algorithm complexity affecting the time performance. If simple...
Pose estimation is one of the most important part of guidance systems in unmanned vehicles. In this paper, we consider the problem of pose estimation of unmanned ground vehicle (UGV) equipped with a global positioning system (GPS), an odometer and an electronic compass. The unscented Kalman filter (UKF) is used to fuse the information from those sensors. To improve the accuracy and robustness, we...
Although high-resolution remote sensing (RS) images can supplement the reference map's deficiency in LADAR (Laser Radar) referenced navigation system over plain area, their different imaging modes still leave great errors' introduction into the positioning process. As positioning is commonly accomplished by matching the edges extracted as the obvious terrain characteristics, exploring automatic procedure...
The problem of convoy following includes automatic speed and steering control of an intelligent vehicle following the motion of a lead vehicle. In this paper, we describe a vision-based scheme to achieve convoy following in real-time. Our approach has been based on Kalman-Bucy filtering (KBF) requiring a simple vision cue. KBF allows recursive estimation of speed and steering as new image measurements...
This research deals with the problem of navigation for an industrial automated guided vehicle (AGV). The vehicle navigates by using a combination of odometric dead reckoning (DR) and position measurements from known landmarks. The measurement system could be a video camera, optical or radio frequency triangulation, sonar image mapping or others. A parameter identification system is used to identify...
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