1 code implementation • 3 Mar 2023 • You Shen, Yunzhou Zhang, Yanmin Wu, Zhenyu Wang, Linghao Yang, Sonya Coleman, Dermot Kerr
Specifically, we design the Pillar-based Shape Completion (PSC) module to predict the probability of occupancy whether a pillar contains object shapes.
no code implementations • 18 Oct 2021 • Rui Tian, Yunzhou Zhang, Yonghui Feng, Linghao Yang, Zhenzhong Cao, Sonya Coleman, Dermot Kerr
To solve this problem, we propose a quadric initialization method based on the decoupling of the quadric parameters method, which improves the robustness to observation noise.
no code implementations • 15 Jan 2021 • Rui Tian, Yunzhou Zhang, Delong Zhu, Shiwen Liang, Sonya Coleman, Dermot Kerr
In this paper, with the assumption of a constant height of the camera above the ground, we develop a light-weight scale recovery framework leveraging an accurate and robust estimation of the ground plane.
no code implementations • 6 Nov 2020 • Delei Kong, Zheng Fang, Haojia Li, Kuanxu Hou, Sonya Coleman, Dermot Kerr
In this paper, we propose an end-to-end visual place recognition network for event cameras, which can achieve good place recognition performance in challenging environments.
2 code implementations • 27 Apr 2020 • Yanmin Wu, Yunzhou Zhang, Delong Zhu, Yonghui Feng, Sonya Coleman, Dermot Kerr
Object-level data association and pose estimation play a fundamental role in semantic SLAM, which remain unsolved due to the lack of robust and accurate algorithms.
no code implementations • 2 Jul 2018 • Diederik Paul Moeys, Daniel Neil, Federico Corradi, Emmett Kerr, Philip Vance, Gautham Das, Sonya A. Coleman, Thomas M. McGinnity, Dermot Kerr, Tobi Delbruck
Conventional vision CNNs are driven by camera frames at constant sample rate, thus achieving a fixed latency and power consumption tradeoff.
no code implementations • 30 Jun 2016 • Diederik Paul Moeys, Federico Corradi, Emmett Kerr, Philip Vance, Gautham Das, Daniel Neil, Dermot Kerr, Tobi Delbruck
The CNN is trained and run on data from a Dynamic and Active Pixel Sensor (DAVIS) mounted on a Summit XL robot (the predator), which follows another one (the prey).