5 accepted papers
Globally localizing a mobile robot in a known map is often a foundation for enabling robots to navigate and operate autonomously. In indoor environments, traditional Monte Carlo localization based on occupancy grid maps is considered the gold standard, but its accuracy is limited by the representati
Many autonomous robotic applications require object-level understanding when deployed. Actively reconstructing objects of interest, i.e. objects with specific semantic meanings, is therefore relevant for a robot to perform downstream tasks in an initially unknown environment. In this work, we propos…
Determining the state of a mobile robot is an essential building block of robot navigation systems. In this letter, we address the problem of estimating the robot's pose in an indoor environment using 2D LiDAR data and investigate how modern environment models can improve gold standard Monte-Carlo l
Mapping an environment is essential for several robotic tasks, particularly for localization. In this letter, we address the problem of mapping the environment using LiDAR point clouds with the goal to obtain a map representation that is well suited for robot localization. To this end, we utilize a
We propose a novel pose estimation method for geometric vision of omni-directional cameras. On the basis of the regularity of the pixel movement after camera pose changes, we formulate and prove the sinusoidal relationship between pixels movement and camera motion. We use the improved Fourier-Mellin…