68 citations · 204 across the 20 of their papers we have counts for
20 papers
SubT-MRS Dataset: Pushing SLAM Towards All-weather Environments
Shibo Zhao, Yuanjun Gao, Tianhao Wu +18
Simultaneous localization and mapping (SLAM) is a fundamental task for numerous applications such as autonomous navigation and exploration. Despite many SLAM datasets have been rel…
Learning Risk-Aware Costmaps via Inverse Reinforcement Learning for Off-Road Navigation
Samuel Triest, Mateo Guaman Castro, Parv Maheshwari +3
The process of designing costmaps for off-road driving tasks is often a challenging and engineering-intensive task. Recent work in costmap design for off-road driving focuses on tr…
PyPose: A Library for Robot Learning with Physics-based Optimization
Chen Wang, Dasong Gao, Kuan Xu +34
Deep learning has had remarkable success in robotic perception, but its data-centric nature suffers when it comes to generalizing to ever-changing environments. By contrast, physic…
How Does It Feel? Self-Supervised Costmap Learning for Off-Road Vehicle Traversability
Mateo Guaman Castro, Samuel Triest, Wenshan Wang +4
Estimating terrain traversability in off-road environments requires reasoning about complex interaction dynamics between the robot and these terrains. However, it is challenging to…
DytanVO: Joint Refinement of Visual Odometry and Motion Segmentation in Dynamic Environments
Shihao Shen, Yilin Cai, Wenshan Wang +1
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…
TartanDrive: A Large-Scale Dataset for Learning Off-Road Dynamics Models
Samuel Triest, Matthew Sivaprakasam, Sean J. Wang +3
We present TartanDrive, a large scale dataset for learning dynamics models for off-road driving. We collected a dataset of roughly 200,000 off-road driving interactions on a modifi…
COMPASS: Contrastive Multimodal Pretraining for Autonomous Systems
Shuang Ma, Sai Vemprala, Wenshan Wang +4
Learning representations that generalize across tasks and domains is challenging yet necessary for autonomous systems. Although task-driven approaches are appealing, designing mode…
Unsupervised Online Learning for Robotic Interestingness with Visual Memory
Chen Wang, Yuheng Qiu, Wenshan Wang +3
Autonomous robots frequently need to detect "interesting" scenes to decide on further exploration, or to decide which data to share for cooperation. These scenarios often require f…
AirDOS: Dynamic SLAM benefits from Articulated Objects
Yuheng Qiu, Chen Wang, Wenshan Wang +2
Dynamic Object-aware SLAM (DOS) exploits object-level information to enable robust motion estimation in dynamic environments. Existing methods mainly focus on identifying and exclu…
VDB-EDT: An Efficient Euclidean Distance Transform Algorithm Based on VDB Data Structure
Delong Zhu, Chaoqun Wang, Wenshan Wang +3
This paper presents a fundamental algorithm, called VDB-EDT, for Euclidean distance transform (EDT) based on the VDB data structure. The algorithm executes on grid maps and generat…
ORStereo: Occlusion-Aware Recurrent Stereo Matching for 4K-Resolution Images
Yaoyu Hu, Wenshan Wang, Huai Yu +2
Stereo reconstruction models trained on small images do not generalize well to high-resolution data. Training a model on high-resolution image size faces difficulties of data avail…
TartanVO: A Generalizable Learning-based VO
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 challengin…
Visual Memorability for Robotic Interestingness via Unsupervised Online Learning
Chen Wang, Wenshan Wang, Yuheng Qiu +2
In this paper, we explore the problem of interesting scene prediction for mobile robots. This area is currently underexplored but is crucial for many practical applications such as…
TartanAir: A Dataset to Push the Limits of Visual SLAM
Wenshan Wang, Delong Zhu, Xiangwei Wang +6
We present a challenging dataset, the TartanAir, for robot navigation tasks and more. The data is collected in photo-realistic simulation environments with the presence of moving o…
Autonomous Aerial Cinematography In Unstructured Environments With Learned Artistic Decision-Making
Rogerio Bonatti, Wenshan Wang, Cherie Ho +6
Aerial cinematography is revolutionizing industries that require live and dynamic camera viewpoints such as entertainment, sports, and security. However, safely piloting a drone wh…
Can a Robot Become a Movie Director? Learning Artistic Principles for Aerial Cinematography
Mirko Gschwindt, Efe Camci, Rogerio Bonatti +3
Aerial filming is constantly gaining importance due to the recent advances in drone technology. It invites many intriguing, unsolved problems at the intersection of aesthetical and…
Towards a Robust Aerial Cinematography Platform: Localizing and Tracking Moving Targets in Unstructured Environments
Rogerio Bonatti, Cherie Ho, Wenshan Wang +2
The use of drones for aerial cinematography has revolutionized several applications and industries that require live and dynamic camera viewpoints such as entertainment, sports, an…
Improved Generalization of Heading Direction Estimation for Aerial Filming Using Semi-supervised Regression
Wenshan Wang, Aayush Ahuja, Yanfu Zhang +2
In the task of Autonomous aerial filming of a moving actor (e.g. a person or a vehicle), it is crucial to have a good heading direction estimation for the actor from the visual inp…
Integrating kinematics and environment context into deep inverse reinforcement learning for predicting off-road vehicle trajectories
Yanfu Zhang, Wenshan Wang, Rogerio Bonatti +2
Predicting the motion of a mobile agent from a third-person perspective is an important component for many robotics applications, such as autonomous navigation and tracking. With a…
Autonomous drone cinematographer: Using artistic principles to create smooth, safe, occlusion-free trajectories for aerial filming
Rogerio Bonatti, Yanfu Zhang, Sanjiban Choudhury +2
Autonomous aerial cinematography has the potential to enable automatic capture of aesthetically pleasing videos without requiring human intervention, empowering individuals with th…