首页|Studies from Beijing Institute of Technology Further Understanding of Robotics ( W-VSLAM: A Visual Mapping Algorithm for Indoor Inspection Robots)
Studies from Beijing Institute of Technology Further Understanding of Robotics ( W-VSLAM: A Visual Mapping Algorithm for Indoor Inspection Robots)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Data detailed on robotics have been presented. Ac cording to news reporting from Beijing, People's Republic of China, by NewsRx jo urnalists, research stated, "In recent years, with the widespread application of indoor inspection robots, high-precision, robust environmental perception has b ecome essential for robotic mapping." The news editors obtained a quote from the research from Beijing Institute of Te chnology: "Addressing the issues of visual-inertial estimation inaccuracies due to redundant pose degrees of freedom and accelerometer drift during the planar m otion of mobile robots in indoor environments, we propose a visual SLAM percepti on method that integrates wheel odometry information. First, the robot's body po se is parameterized in SE(2) and the corresponding camera pose is parameterized in SE(3). On this basis, we derive the visual constraint residuals and their Jac obian matrices for reprojection observations using the camera projection model. We employ the concept of pre-integration to derive pose-constraint residuals and their Jacobian matrices and utilize marginalization theory to derive the relati ve pose residuals and their Jacobians for loop closure constraints. This approac h solves the nonlinear optimization problem to obtain the optimal pose and landm ark points of the ground-moving robot. A comparison with the ORBSLAM3 algorithm reveals that, in the recorded indoor environment datasets, the proposed algorith m demonstrates significantly higher perception accuracy, with root mean square e rror (RMSE) improvements of 89.2% in translation and 98.5% in rotation for absolute trajectory error (ATE)."
Beijing Institute of TechnologyBeijingPeople's Republic of ChinaAsiaAlgorithmsEmerging TechnologiesMachine L earningNano-robotRobotRobotics
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