Search Results for author:
Found 18 papers, 7 papers with code
Deep Bayesian Future Fusion for Self-Supervised, High-Resolution, Off-Road Mapping
The limited sensing resolution of resource-constrained off-road vehicles poses significant challenges towards reliable off-road autonomy.
How Does It Feel? Self-Supervised Costmap Learning for Off-Road Vehicle Traversability
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.
DytanVO: Joint Refinement of Visual Odometry and Motion Segmentation in Dynamic Environments
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.
Unsupervised Online Learning for Robotic Interestingness with Visual Memory
Instead, we propose to develop a method that automatically adapts online to the environment to report interesting scenes quickly.
AirDOS: Dynamic SLAM benefits from Articulated Objects
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.
ORStereo: Occlusion-Aware Recurrent Stereo Matching for 4K-Resolution Images
Stereo reconstruction models trained on small images do not generalize well to high-resolution data.
Risk Prediction on Traffic Accidents using a Compact Neural Model for Multimodal Information Fusion over Urban Big Data
Predicting risk map of traffic accidents is vital for accident prevention and early planning of emergency response.
TartanVO: A Generalizable Learning-based VO
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.
Visual Memorability for Robotic Interestingness via Unsupervised Online Learning
In this paper, we explore the problem of interesting scene prediction for mobile robots.
TartanAir: A Dataset to Push the Limits of Visual SLAM
We present a challenging dataset, the TartanAir, for robot navigation task and more.
Robotics
Autonomous Aerial Cinematography In Unstructured Environments With Learned Artistic Decision-Making
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.
Learning Temporal Abstraction with Information-theoretic Constraints for Hierarchical Reinforcement Learning
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
Towards a Robust Aerial Cinematography Platform: Localizing and Tracking Moving Targets in Unstructured Environments
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.
Can a Robot Become a Movie Director? Learning Artistic Principles for Aerial Cinematography
Aerial filming is constantly gaining importance due to the recent advances in drone technology.
Improved Generalization of Heading Direction Estimation for Aerial Filming Using Semi-supervised Regression
We show that by leveraging unlabeled sequences, the amount of labeled data required can be significantly reduced.
Integrating kinematics and environment context into deep inverse reinforcement learning for predicting off-road vehicle trajectories
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.
Autonomous drone cinematographer: Using artistic principles to create smooth, safe, occlusion-free trajectories for aerial filming
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.
Neural Aesthetic Image Reviewer
Through multi-task learning, the proposed models can rate aesthetic images as well as produce comments in an end-to-end manner.
