3 accepted papers
Reliable scene understanding requires not only accurate predictions but also well-calibrated confidence estimates to ensure calibrated uncertainty estimation, especially in safety-critical domains like autonomous driving. In this context, semantic segmentation of LiDAR points supports real-time 3D s…
Mapping with uncertainty representation is required in many research domains, especially for localization. Although there are many investigations regarding the uncertainty of the pose estimation of an ego-robot with map information, the quality of the reference maps is often neglected. To avoid pote
We present a novel interval-based visual-inertial LiDAR SLAM (i-VIL SLAM) method that solely assumes sensor errors to be bounded and propagates the error from the input sources to the estimated map and trajectory using interval analysis. The method allows us to restrict the solution set of the robot…