5 accepted papers
In this study, we propose an object shape alignment method through a robust optimization scheme for 6-degrees-of-freedom (DOF) object pose compensation. Although the pose estimation of the 3D object by the camera has been rapidly improved in recent years with the development of deep learning, the es…
Event cameras are asynchronous neuromorphic vision sensors with high temporal resolution and no motion blur, offering advantages over standard frame-based cameras especially in high-speed motions and high dynamic range conditions. However, event cameras are unable to capture the overall context of t…
In the presence of fast motion, point clouds obtained from mechanical spinning LiDAR can be easily distorted due to the slow scanning speed of the LiDAR. Existing LiDAR-only odometry algorithms generally ignore this distortion or compensate by linearly interpolating the estimated relative motion bet…
In this paper, we propose a novel object-based visual-inertial navigation system fully embedded in a matrix Lie group and built upon the invariant Kalman filtering theory. Specifically, we focus on relative pose measurements of objects and derive an error equation at the associated tangent space. We…
In this paper, we propose a novel filtering-based method that fuses events from a dynamic vision sensor (DVS), images, and inertial measurements to estimate camera poses. A DVS is a bio-inspired sensor that generates events triggered by brightness changes. It can cover the drawbacks of a conventiona…