Current reinforcement learning (RL) in robotics often experiences difficulty in generalizing to new downstream tasks due to the innate task-specific training paradigm.
Deep reinforcement learning has enabled robots to learn motor skills from environmental interactions with minimal to no prior knowledge.
Deep neural networks have been widely studied in autonomous driving applications such as semantic segmentation or depth estimation.
Previous studies on image classification have mainly focused on the performance of the networks, not on real-time operation or model compression.
In this paper, we show that existing self-supervised methods do not perform well on depth estimation and propose a gradient-based self-supervised learning algorithm with momentum contrastive loss to help ConvNets extract the geometric information with unlabeled images.
The proposed algorithm allows to separate the moving object detection and visual odometry (VO) so that an arbitrary robust VO method can be employed in a dynamic situation with a combination of moving object detection, whereas other VO algorithms for a dynamic environment are inseparable.
With the dominance of keyframe-based SLAM in the field of robotics, the relative frame poses between keyframes have typically been sacrificed for a faster algorithm to achieve online applications.
The proposed method actively guides the motion of a cinematographer drone so that the color of a target is well-distinguished against the colors of the background in the view of the drone.
The proposed system includes 1) a target motion prediction module which can be applied to dense environments and 2) a hierarchical chasing planner based on a proposed metric for visibility.
Further, we use a dual-mode motion model to consistently distinguish between the static and dynamic parts in the temporal motion tracking stage.
In this work, we propose an edge detection algorithm by estimating a lifetime of an event produced from dynamic vision sensor (DVS), also known as event camera.
This work deals with a moving target chasing mission of an aerial vehicle equipped with a vision sensor in a cluttered environment.
We propose a novel approach to estimate the three degrees of freedom (DoF) drift-free rotational motion of an RGB-D camera from only a single line and plane in the Manhattan world (MW).