首页|Recent Research from Autonomous University of Baja California Highlight Findings in Robotics (Multi-view 3d Data Fusion and Patching To Reduce Shannon Entropy I n Robotic Vision)
Recent Research from Autonomous University of Baja California Highlight Findings in Robotics (Multi-view 3d Data Fusion and Patching To Reduce Shannon Entropy I n Robotic Vision)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Data detailed on Robotics have been pr esented. According to news originating from Baja California, Mexico, by NewsRx c orrespondents, research stated, "Optical Sensors Fusion is intended to enrich th e data obtained from Robotic Vision systems, which play a crucial role in applic ations such as machine guidance and monitoring. This paper presents a data augme ntation method that uniquely combines cameras with the rotational wide-based Las er Scanner Technical Vision System (LSTVS), an innovative system not previously explored in conjunction with other 3D data acquisition systems." Our news journalists obtained a quote from the research from the Autonomous Univ ersity of Baja California, "This combined work aims to address common issues in 3D reconstruction by utilizing a deterministic position estimation from the lase r scanner complementary to probabilistic estimations from stereo vision. This ap proach aims to reduce informational entropy in regions where data is lacking or difficult to interpret, primarily due to the inherent limitations of Robotic Vis ion systems. An LSTVS with stereo cameras prototype was calibrated using intrins ic and extrinsic parameters of the cameras and laser scanner components, enablin g laser positioning over selected interest point and areas from the stereo 3D da ta. By fusing 3D data from both systems, data quality is improved on challenging surfaces often problematic for stereo vision, like low texture or nonLambertian surfaces. Experiments aim to test the stereo system limits in order to fuse the obtained data. Multiple experiments with variable parameters (angle of view, st riking distance, most indicative kinds of the obstacle's refractive surface amon g them) are described in order to prove new abilities of the proposed combined R V."
Baja CaliforniaMexicoNorth and Centr al AmericaEmerging TechnologiesMachine LearningRoboticsRobotsAutonomou s University of Baja California
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