no code implementations • 22 Sep 2022 • Mateo Guaman Castro, Samuel Triest, Wenshan Wang, Jason M. Gregory, Felix Sanchez, John G. Rogers III, Sebastian Scherer
Our short-scale navigation results show that using our learned costmaps leads to overall smoother navigation, and provides the robot with a more fine-grained understanding of the robot-terrain interactions.
2 code implementations • 17 Sep 2022 • Shihao Shen, Yilin Cai, Wenshan Wang, Sebastian Scherer
Learning-based visual odometry (VO) algorithms achieve remarkable performance on common static scenes, benefiting from high-capacity models and massive annotated data, but tend to fail in dynamic, populated environments.
1 code implementation • 18 Nov 2021 • Chen Wang, Yuheng Qiu, Wenshan Wang, Yafei Hu, Seungchan Kim, Sebastian Scherer
Instead, we propose to develop a method that automatically adapts online to the environment to report interesting scenes quickly.
1 code implementation • 21 Sep 2021 • Yuheng Qiu, Chen Wang, Wenshan Wang, Mina Henein, Sebastian Scherer
To the best of our knowledge, AirDOS is the first dynamic object-aware SLAM system demonstrating that camera pose estimation can be improved by incorporating dynamic articulated objects.
no code implementations • 13 Mar 2021 • Yaoyu Hu, Wenshan Wang, Huai Yu, Weikun Zhen, Sebastian Scherer
Stereo reconstruction models trained on small images do not generalize well to high-resolution data.
no code implementations • 21 Feb 2021 • Wenshan Wang, Su Yang, Weishan Zhang
Predicting risk map of traffic accidents is vital for accident prevention and early planning of emergency response.
2 code implementations • 31 Oct 2020 • Wenshan Wang, Yaoyu Hu, Sebastian Scherer
We present the first learning-based visual odometry (VO) model, which generalizes to multiple datasets and real-world scenarios and outperforms geometry-based methods in challenging scenes.
3 code implementations • ECCV 2020 • Chen Wang, Wenshan Wang, Yuheng Qiu, Yafei Hu, Sebastian Scherer
In this paper, we explore the problem of interesting scene prediction for mobile robots.
1 code implementation • 31 Mar 2020 • Wenshan Wang, Delong Zhu, Xiangwei Wang, Yaoyu Hu, Yuheng Qiu, Chen Wang, Yafei Hu, Ashish Kapoor, Sebastian Scherer
We present a challenging dataset, the TartanAir, for robot navigation task and more.
no code implementations • 15 Oct 2019 • Rogerio Bonatti, Wenshan Wang, Cherie Ho, Aayush Ahuja, Mirko Gschwindt, Efe Camci, Erdal Kayacan, Sanjiban Choudhury, Sebastian Scherer
In this work, we address the problem in its entirety and propose a complete system for real-time aerial cinematography that for the first time combines: (1) vision-based target estimation; (2) 3D signed-distance mapping for occlusion estimation; (3) efficient trajectory optimization for long time-horizon camera motion; and (4) learning-based artistic shot selection.
no code implementations • 25 Sep 2019 • Wenshan Wang, Yaoyu Hu, Sebastian Scherer
A visualization of the latent space demonstrates that our algorithm learns an effective abstraction of the long action sequences.
Hierarchical Reinforcement Learning reinforcement-learning +1
no code implementations • 4 Apr 2019 • Rogerio Bonatti, Cherie Ho, Wenshan Wang, Sanjiban Choudhury, Sebastian Scherer
In this work, we overcome such limitations and propose a complete system for aerial cinematography that combines: (1) a vision-based algorithm for target localization; (2) a real-time incremental 3D signed-distance map algorithm for occlusion and safety computation; and (3) a real-time camera motion planner that optimizes smoothness, collisions, occlusions and artistic guidelines.
no code implementations • 4 Apr 2019 • Mirko Gschwindt, Efe Camci, Rogerio Bonatti, Wenshan Wang, Erdal Kayacan, Sebastian Scherer
Aerial filming is constantly gaining importance due to the recent advances in drone technology.
no code implementations • 26 Mar 2019 • Wenshan Wang, Aayush Ahuja, Yanfu Zhang, Rogerio Bonatti, Sebastian Scherer
We show that by leveraging unlabeled sequences, the amount of labeled data required can be significantly reduced.
1 code implementation • 16 Oct 2018 • Yanfu Zhang, Wenshan Wang, Rogerio Bonatti, Daniel Maturana, Sebastian Scherer
The first-stage network learns feature representations of the environment using low-level LiDAR statistics and the second-stage network combines those learned features with kinematics data.
no code implementations • 28 Aug 2018 • Rogerio Bonatti, yanfu Zhang, Sanjiban Choudhury, Wenshan Wang, Sebastian Scherer
Autonomous aerial cinematography has the potential to enable automatic capture of aesthetically pleasing videos without requiring human intervention, empowering individuals with the capability of high-end film studios.
no code implementations • 28 Feb 2018 • Wenshan Wang, Su Yang, Weishan Zhang, Jiulong Zhang
Through multi-task learning, the proposed models can rate aesthetic images as well as produce comments in an end-to-end manner.