6 accepted papers
To solve the problem of inaccurate orientation estimation after long-term operations of the Inertial Measurement Unit (IMU), we present a learning-based method (called Gyro-Net) to estimate and compensate for IMU gyroscope random errors. We firstly introduce a semi-dense network structure, which ext
Event cameras have the potential to overcome the limitations of classical computer vision in real-world applications. Depth estimation is a crucial step for high-level robotics tasks and has attracted much attention from the community. In this paper, we propose an event-based dense depth estimation…
In recent years, self-supervised paradigms for depth estimation have drawn lots of attention from the community. Promising as they are, in order to achieve wider application and better performance, reasoning about the uncertainty of the estimated depth maps is in great demand. In this paper, we prop
Programming cooperative tasks for autonomous swarm robotic systems has always been challenging. In this paper, we introduce a concept ‘Actor’, as a virtualization for robot platforms. Every robot platform in the swarm robotic system carries out the task and interacts with others as an Actor. We desi…
Recently, the emerging bio-inspired event cameras have demonstrated potentials for a wide range of robotic applications in dynamic environments. In this paper, we propose a novel fast and asynchronous event-based corner detection method which is called FA-Harris. FA-Harris consists of several compon…
We propose a novel monocular visual odometry (VO) system called UnDeepVO in this paper. UnDeepVO is able to estimate the 6-DoF pose of a monocular camera and the depth of its view by using deep neural networks. There are two salient features of the proposed UnDeepVo:one is the unsupervised deep lear…