首页|Reports Summarize Robotics and Automation Study Results from Tongji University ( Log-lio2: a Lidar-inertial Odometry With Efficient Uncertainty Analysis)
Reports Summarize Robotics and Automation Study Results from Tongji University ( Log-lio2: a Lidar-inertial Odometry With Efficient Uncertainty Analysis)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Current study results on Robotics-Ro botics and Automation have been published. According to news originating from Sh anghai, People's Republic of China, by NewsRx correspondents, research stated, " Uncertainty in LiDAR measurements, stemming from factors such as range sensing, is crucial for LIO (LiDAR-Inertial Odometry) systems as it affects the accurate weighting in the loss function. While recent LIO systems address uncertainty rel ated to range sensing, the impact of incident angle on uncertainty is often over looked by the community." Financial support for this research came from National Key Research & Development Program of China. Our news journalists obtained a quote from the research from Tongji University, "Moreover, the existing uncertainty propagation methods suffer from computationa l inefficiency. This letter proposes a comprehensive point uncertainty model that accounts for both the uncertainties from LiDAR measurements and surface charac teristics, along with an efficient local uncertainty analytical method for LiDAR -based state estimation problem. We employ a projection operator that separates the uncertainty into the ray direction and its orthogonal plane. Then, we derive incremental Jacobian matrices of eigenvalues and eigenvectors w.r.t. points, wh ich enables a fast approximation of uncertainty propagation. This approach elimi nates the requirement for redundant traversal of points, significantly reducing the time complexity of uncertainty propagation from O (n) O(1) when a new point is added."
ShanghaiPeople's Republic of ChinaAs iaRobotics and AutomationRoboticsTongji University
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