6 accepted papers
Autonomous landing on mobile platforms is crucial for extending quadcopter operational flexibility, yet conventional methods are often too inefficient for highly dynamic scenarios. The core limitation lies in the prevalent “track-then-descend” paradigm, which treats the platform as a passive target
Simultaneous Localization and Mapping (SLAM) plays an important role in robot autonomy. Reliability and efficiency are the two most valued features for applying SLAM in robot applications. In this letter, we consider achieving a reliable LiDAR-based SLAM function in computation-limited platforms, su
Localizing an aggressively moving platform is a considerable challenge in the SLAM domain. This paper presents a dataset, SwiftBase, crafted to facilitate research into precise localization under such conditions. It includes high-speed cameras with over 200Hz sampling rate, capturing detailed visual…
Accurate and timely onboard perception is a prerequisite for mobile robots to operate in highly dynamic scenarios. The bio-inspired event camera can capture more motion details than a traditional camera by triggering each pixel asynchronously and therefore is more suitable in such scenarios. Among v…
In recent years, thanks to the continuously reduced cost and weight of 3D lidar, the applications of this type of sensor in the community have become increasingly popular. Despite many progresses, estimation drift and tracking loss are still prevalent concerns associated with these systems. However,…
In this letter we investigate a tightly coupled Lidar-Inertia Odometry and Mapping (LIOM) scheme, with the capability to incorporate multiple lidars with complementary field of view (FOV). In essence, we devise a time-synchronized scheme to combine extracted features from separate lidars into a sing