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Found 82 papers, 33 papers with code
Direct Monocular Odometry Using Points and Lines
Most visual odometry algorithm for a monocular camera focuses on points, either by feature matching, or direct alignment of pixel intensity, while ignoring a common but important geometry entity: edges.
Pop-up SLAM: Semantic Monocular Plane SLAM for Low-texture Environments
In this paper, we propose real-time monocular plane SLAM to demonstrate that scene understanding could improve both state estimation and dense mapping especially in low-texture environments.
Obstacle Avoidance through Deep Networks based Intermediate Perception
Second, predict the trajectory from the depth and normal.
Near-Optimal Edge Evaluation in Explicit Generalized Binomial Graphs
By leveraging this property, we are able to significantly reduce computational complexity from exponential to linear in the number of edges.
Robotics
Learning Heuristic Search via Imitation
In this paper, we do so by training a heuristic policy that maps the partial information from the search to decide which node of the search tree to expand.
Semantic 3D Occupancy Mapping through Efficient High Order CRFs
In the paper, we propose an incremental and (near) real-time semantic mapping system.
Improving Stochastic Policy Gradients in Continuous Control with Deep Reinforcement Learning using the Beta Distribution
Recently, reinforcement learning with deep neural networks has achieved great success in challenging continuous control problems such as 3D locomotion and robotic manipulation.
Bayesian Active Edge Evaluation on Expensive Graphs
We are interested in planning algorithms that actively infer the underlying structure of the valid configuration space during planning in order to find solutions with minimal effort.
Hindsight is Only 50/50: Unsuitability of MDP based Approximate POMDP Solvers for Multi-resolution Information Gathering
The contribution of the paper helps identify the properties of a POMDP problem for which the use of MDP based POMDP solvers is inappropriate, enabling better design choices.
CubeSLAM: Monocular 3D Object SLAM
Objects can provide long-range geometric and scale constraints to improve camera pose estimation and reduce monocular drift.
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.
Monocular Object and Plane SLAM in Structured Environments
In this paper, we present a monocular Simultaneous Localization and Mapping (SLAM) algorithm using high-level object and plane landmarks.
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.
Joint Point Cloud and Image Based Localization For Efficient Inspection in Mixed Reality
While external pose estimation and fiducial marker based localization would require setup, maintenance, and manual calibration; marker-free self-localization can be achieved using the onboard depth sensor and camera.
Robotics
RGB-D SLAM in Dynamic Environments Using Point Correlations
This method utilizes the correlation between map points to separate points that are part of the static scene and points that are part of different moving objects into different groups.
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.
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.
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.
Automatic Real-time Anomaly Detection for Autonomous Aerial Vehicles
The result is an easy-to-deploy anomaly detection method that does not assume a specific aircraft model and can detect many types of faults and anomalies in a wide range of autonomous aircraft.
Systems and Control Robotics Systems and Control
ALFA: A Dataset for UAV Fault and Anomaly Detection
We have also provided the helper tools in several programming languages to load and work with the data and to help the evaluation of a detection method using the dataset.
Learning Visuomotor Policies for Aerial Navigation Using Cross-Modal Representations
We analyze the rich latent spaces learned with our proposed representations, and show that the use of our cross-modal architecture significantly improves control policy performance as compared to end-to-end learning or purely unsupervised feature extractors.
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
A Stereo Algorithm for Thin Obstacles and Reflective Objects
We present a hierarchical disparity algorithm that reduces noise, separately identify wires using semantic object triangulation in three images, and use information about the polarization of light to estimate the disparity of reflective objects.
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.
Line-based Camera Pose Estimation in Point Cloud of Structured Environments
In this paper, we propose a new 2D-3D registration method to estimate 2D-3D line feature correspondences and the camera pose in untextured point clouds of structured environments.
Deep-Learning Assisted High-Resolution Binocular Stereo Depth Reconstruction
The proposed deep-learning assisted method is evaluated on the Middlebury dataset and high-resolution stereo images collected by our customized binocular stereo camera.
TartanAir: A Dataset to Push the Limits of Visual SLAM
We present a challenging dataset, the TartanAir, for robot navigation task and more.
Robotics
Monocular Camera Localization in Prior LiDAR Maps with 2D-3D Line Correspondences
With the pose prediction from VIO, we can efficiently obtain coarse 2D-3D line correspondences.
Visual Memorability for Robotic Interestingness via Unsupervised Online Learning
In this paper, we explore the problem of interesting scene prediction for mobile robots.
Lifelong Graph Learning
In this paper, we bridge GNN and lifelong learning by converting a continual graph learning problem to a regular graph learning problem so GNN can inherit the lifelong learning techniques developed for convolutional neural networks (CNN).
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.
ULSD: Unified Line Segment Detection across Pinhole, Fisheye, and Spherical Cameras
Targeting at the unified line segment detection (ULSD) for both distorted and undistorted images, we propose to represent line segments with the Bezier curve model.
Ranked #5 on
Line Segment Detection
on wireframe dataset
(sAP10 metric)
Do You See What I See? Coordinating Multiple Aerial Cameras for Robot Cinematography
We validate our approach in multiple cluttered environments of a photo-realistic simulator, and deploy the system using two UAVs in real-world experiments.
Integration of Fully-Actuated Multirotors into Real-World Applications
The strategies are lightweight, simple, and allow rapid integration of the available tools with these new vehicles for the fast development of new real-world applications.
Batteries, camera, action! Learning a semantic control space for expressive robot cinematography
First, we generate a database of video clips with a diverse range of shots in a photo-realistic simulator, and use hundreds of participants in a crowd-sourcing framework to obtain scores for a set of semantic descriptors for each clip.
Bridging Graph Network to Lifelong Learning with Feature Interaction
In this paper, we aim to bridge GNN to lifelong learning, which is to overcome the effect of ``catastrophic forgetting" for continuously learning a sequence of graph-structured tasks.
Real-Time Ellipse Detection for Robotics Applications
We propose a new algorithm for real-time detection and tracking of elliptic patterns suitable for real-world robotics applications.
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.
3D Segmentation Learning from Sparse Annotations and Hierarchical Descriptors
One of the main obstacles to 3D semantic segmentation is the significant amount of endeavor required to generate expensive point-wise annotations for fully supervised training.
i3dLoc: Image-to-range Cross-domain Localization Robust to Inconsistent Environmental Conditions
Based on such features, we further design a spherical convolution network to learn viewpoint-invariant symmetric place descriptors.
CVaR-based Flight Energy Risk Assessment for Multirotor UAVs using a Deep Energy Model
Computing the CVaR on the risk-space distribution provides a metric that can evaluate the overall risk of a flight before take-off.
3D Human Reconstruction in the Wild with Collaborative Aerial Cameras
Aerial vehicles are revolutionizing applications that require capturing the 3D structure of dynamic targets in the wild, such as sports, medicine, and entertainment.
3D-SiamRPN: An End-to-End Learning Method for Real-Time 3D Single Object Tracking Using Raw Point Cloud
Then, to fuse the information of features in the two branches and obtain their similarity, we propose two cross correlation modules, named Pointcloud-wise and Point-wise respectively.
Unified Representation of Geometric Primitives for Graph-SLAM Optimization Using Decomposed Quadrics
We first present a unified representation based on quadrics, leading to a consistent and concise formulation.
AirLoop: Lifelong Loop Closure Detection
Nevertheless, simply finetuning the model on new data is infeasible since it may cause the model's performance on previously learned data to degrade over time, which is also known as the problem of catastrophic forgetting.
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.
Semantically Consistent Image-to-Image Translation for Unsupervised Domain Adaptation
Unsupervised Domain Adaptation (UDA) aims to adapt models trained on a source domain to a new target domain where no labelled data is available.
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.
Drone flight data reveal energy and greenhouse gas emissions savings for small package delivery
The adoption of Uncrewed Aerial Vehicles (UAVs) for last-mile deliveries will affect the energy productivity of package delivery and require new methods to understand the associated energy consumption and greenhouse gas (GHG) emissions.
AdaFusion: Visual-LiDAR Fusion with Adaptive Weights for Place Recognition
Recent years have witnessed the increasing application of place recognition in various environments, such as city roads, large buildings, and a mix of indoor and outdoor places.
AirObject: A Temporally Evolving Graph Embedding for Object Identification
In this context, we propose a novel temporal 3D object encoding approach, dubbed AirObject, to obtain global keypoint graph-based embeddings of objects.
AirDet: Few-Shot Detection without Fine-tuning for Autonomous Exploration
Few-shot object detection has attracted increasing attention and rapidly progressed in recent years.
Off-Policy Evaluation with Online Adaptation for Robot Exploration in Challenging Environments
To the best of our knowledge, this work for the first time demonstrates value function prediction on real-world dataset for robot exploration in challenging subterranean and urban environments.
Detection and Physical Interaction with Deformable Linear Objects
On the other hand, while physical interaction with these objects has been done with ground manipulators, there have not been any studies on physical interaction and manipulation of the deformable linear object with aerial robots.
SphereVLAD++: Attention-based and Signal-enhanced Viewpoint Invariant Descriptor
Our previous work provides a viewpoint-invariant descriptor to deal with viewpoint differences; however, the global descriptor suffers from a low signal-noise ratio in unsupervised clustering, reducing the distinguishable feature extraction ability.
AutoMerge: A Framework for Map Assembling and Smoothing in City-scale Environments
We present AutoMerge, a LiDAR data processing framework for assembling a large number of map segments into a complete map.
ALTO: A Large-Scale Dataset for UAV Visual Place Recognition and Localization
We present the ALTO dataset, a vision-focused dataset for the development and benchmarking of Visual Place Recognition and Localization methods for Unmanned Aerial Vehicles.
BioSLAM: A Bio-inspired Lifelong Memory System for General Place Recognition
We present BioSLAM, a lifelong SLAM framework for learning various new appearances incrementally and maintaining accurate place recognition for previously visited areas.
General Place Recognition Survey: Towards the Real-world Autonomy Age
A summary of this work and our datasets and evaluation API is publicly available to the robotics community at: https://github. com/MetaSLAM/GPRS.
Loop Closure Detection
Simultaneous Localization and Mapping
iSimLoc: Visual Global Localization for Previously Unseen Environments with Simulated Images
The visual camera is an attractive device in beyond visual line of sight (B-VLOS) drone operation, since they are low in size, weight, power, and cost, and can provide redundant modality to GPS failures.
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.
MUI-TARE: Multi-Agent Cooperative Exploration with Unknown Initial Position
To intelligently balance the robustness of sub-map merging and exploration efficiency, we develop a new approach for lidar-based multi-agent exploration, which can direct one agent to repeat another agent's trajectory in an \emph{adaptive} manner based on the quality indicator of the sub-map merging process.
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.
AirTrack: Onboard Deep Learning Framework for Long-Range Aircraft Detection and Tracking
Detect-and-Avoid (DAA) capabilities are critical for safe operations of unmanned aircraft systems (UAS).
360FusionNeRF: Panoramic Neural Radiance Fields with Joint Guidance
Additionally, we introduce a semantic consistency loss that encourages realistic renderings of novel views.
TartanCalib: Iterative Wide-Angle Lens Calibration using Adaptive SubPixel Refinement of AprilTags
An accurate calibration of the intrinsics and extrinsics is a critical pre-requisite for using the edge of a wide-angle lens for depth perception and odometry.
Pseudo-Label Noise Suppression Techniques for Semi-Supervised Semantic Segmentation
Current SSL approaches use an initially supervised trained model to generate predictions for unlabelled images, called pseudo-labels, which are subsequently used for training a new model from scratch.
PVT++: A Simple End-to-End Latency-Aware Visual Tracking Framework
Visual object tracking is essential to intelligent robots.
Attention-Enhanced Cross-modal Localization Between 360 Images and Point Clouds
To overcome this limitation, we focus on correlating the information of 360 equirectangular images to point clouds, proposing an end-to-end learnable network to conduct cross-modal visual localization by establishing similarity in high-dimensional feature space.
AirLoc: Object-based Indoor Relocalization
This results in a robust, accurate, and portable indoor relocalization system, which outperforms the state-of-the-art methods in room-level relocalization by 9. 5% of PR-AUC and 7% of accuracy.
Learned Tree Search for Long-Horizon Social Robot Navigation in Shared Airspace
In this work, we propose Social Robot Tree Search (SoRTS), an algorithm for the safe navigation of mobile robots in social domains.
AnyLoc: Towards Universal Visual Place Recognition
In this work, we develop a universal solution to VPR -- a technique that works across a broad range of structured and unstructured environments (urban, outdoors, indoors, aerial, underwater, and subterranean environments) without any re-training or fine-tuning.
Ranked #1 on
Visual Place Recognition
on Nardo-Air R
AirIMU: Learning Uncertainty Propagation for Inertial Odometry
Accurate uncertainty estimation for inertial odometry is the foundation to achieve optimal fusion in multi-sensor systems, such as visual or LiDAR inertial odometry.
Greedy Perspectives: Multi-Drone View Planning for Collaborative Perception in Cluttered Environments
To coordinate teams of aerial robots in filming groups of people in dense environments, a more general view-planning approach is required.
SplaTAM: Splat, Track & Map 3D Gaussians for Dense RGB-D SLAM
Dense simultaneous localization and mapping (SLAM) is crucial for robotics and augmented reality applications.
Toward General-Purpose Robots via Foundation Models: A Survey and Meta-Analysis
Motivated by the impressive open-set performance and content generation capabilities of web-scale, large-capacity pre-trained models (i. e., foundation models) in research fields such as Natural Language Processing (NLP) and Computer Vision (CV), we devote this survey to exploring (i) how these existing foundation models from NLP and CV can be applied to the field of robotics, and also exploring (ii) what a robotics-specific foundation model would look like.
WIT-UAS: A Wildland-fire Infrared Thermal Dataset to Detect Crew Assets From Aerial Views
We present the Wildland-fire Infrared Thermal (WIT-UAS) dataset for long-wave infrared sensing of crew and vehicle assets amidst prescribed wildland fire environments.
A Unified MPC Strategy for a Tilt-rotor VTOL UAV Towards Seamless Mode Transitioning
This paper introduces a unified MPC-based control strategy for a customized tiltrotor VTOL Unmanned Aerial Vehicle (UAV), which does not require mode-switching and can perform the control allocation in a consistent way.
TartanAviation: Image, Speech, and ADS-B Trajectory Datasets for Terminal Airspace Operations
In total, TartanAviation provides 3. 1M images, 3374 hours of Air Traffic Control speech data, and 661 days of ADS-B trajectory data.
Learning Generalizable Feature Fields for Mobile Manipulation
An open problem in mobile manipulation is how to represent objects and scenes in a unified manner, so that robots can use it both for navigating in the environment and manipulating objects.
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.
AirShot: Efficient Few-Shot Detection for Autonomous Exploration
Few-shot object detection has drawn increasing attention in the field of robotic exploration, where robots are required to find unseen objects with a few online provided examples.
