Intelligent vehicles are advancing at a fast speed with the improvement of automation and connectivity, which opens up new possibilities for different cyber-attacks, including in-vehicle …
H Zhan, R Garg, CS Weerasekera… - Proceedings of the …, 2018 - openaccess.thecvf.com
Despite learning based methods showing promising results in single view depth estimation and visual odometry, most existing approaches treat the tasks in a supervised manner …
S Yang, S Scherer - IEEE Transactions on Robotics, 2019 - ieeexplore.ieee.org
In this paper, we present a method for single image three-dimensional (3-D) cuboid object detection and multiview object simultaneous localization and mapping in both static and …
Various autonomous or assisted driving strategies have been facilitated through the accurate and reliable perception of the environment around a vehicle. Among the commonly …
DG Yang, K Jiang, D Zhao, CL Yu, Z Cao… - Science China …, 2018 - Springer
Intelligent connected vehicles (ICVs) are believed to change people's life in the near future by making the transportation safer, cleaner and more comfortable. Although many …
FJ Abdu, Y Zhang, M Fu, Y Li, Z Deng - Sensors, 2021 - mdpi.com
The progress brought by the deep learning technology over the last decade has inspired many research domains, such as radar signal processing, speech and audio recognition …
Monocular visual odometry (VO) suffers severely from error accumulation during frame-to- frame pose estimation. In this paper, we present a self-supervised learning method for VO …
A Singandhupe, HM La - 2019 third IEEE international …, 2019 - ieeexplore.ieee.org
Simultaneous localization and mapping (SLAM) is a widely researched topic in the field of robotics, augmented/virtual reality and more dominantly in self-driving cars. SLAM is a …
Radar is usually more robust than the camera in severe driving scenarios, eg, weak/strong lighting and bad weather. However, unlike RGB images captured by a camera, the semantic …