4 accepted papers
We propose an extension to the LiDAR Odometry and Mapping framework (LOAM) that enables reference object-based trajectory and map optimization. Our approach assumes that the location and geometry of a large reference object are known, e.g., as a CAD model from Building Information Modeling (BIM) or
Real-time 6D pose estimation is a key component for autonomous indoor navigation of Unmanned Aerial Vehicles (UAVs). This letter presents a low-latency LiDAR SLAM framework based on LiDAR scan slicing and concurrent matching, called LoLa-SLAM. Our framework uses sliced point cloud data from a rotati
LiDAR-based odometry and mapping is used in many robotic applications to retrieve the robot's position in an unknown environment and allows for autonomous operation in GPS-denied (e.g., indoor) environments. With a 3D LiDAR sensor, highly accurate localization becomes possible, which enables high qu
In the field of autonomous robotics, Simultaneous Localization and Mapping (SLAM) is still a challenging problem. With cheap visual sensors attracting more and more attention, various solutions to the SLAM problem using visual cues have been proposed. However, current visual SLAM systems are still c…