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Structure from Stereo A Review
The Stanford Cart and the CMU Rover
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algorithm applications approach Artificial Intelligence autonomous calibration cell computer vision Conf constraint correlation corresponding covariance matrix curvature defined depth estimates depth map described detection disparity distance edge edge detection environment equations error extracted Figure function Gaussian geometric global graph IEEE IEEE Int integration and fusion Intell Kalman filter linear matching measurements method mobile robot motion planning multisensor integration navigation node noise object recognition obstacles obtained occluded Occupancy Grid operator optical optical flow optimal orientation pair parameters pixel planar plane points polygonal position primitives problem Proc range data range finder range image regions representation represented robot mapping Robotics and Automat rotation scene Section sensor data Sensor Fusion sequence shape shortest path sonar space spatial stereo stereo vision stereopsis surface surface normal techniques terrain tion uncertainty updated variance vector vertical visibility graph visual voxel world model