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Found 43 papers, 22 papers with code
Grasp, See and Place: Efficient Unknown Object Rearrangement with Policy Structure Prior
For the inner loop, we learn an active seeing policy for self-confident object matching to improve the perception of place.
EDA: Evolving and Distinct Anchors for Multimodal Motion Prediction
Motion prediction is a crucial task in autonomous driving, and one of its major challenges lands in the multimodality of future behaviors.
Semantics-aware Motion Retargeting with Vision-Language Models
Capturing and preserving motion semantics is essential to motion retargeting between animation characters.
DORec: Decomposed Object Reconstruction Utilizing 2D Self-Supervised Features
Decomposing a target object from a complex background while reconstructing is challenging.
Exploiting Point-Wise Attention in 6D Object Pose Estimation Based on Bidirectional Prediction
Traditional geometric registration based estimation methods only exploit the CAD model implicitly, which leads to their dependence on observation quality and deficiency to occlusion.
Leveraging BEV Representation for 360-degree Visual Place Recognition
In addition, the image and point cloud cues can be easily stated in the same coordinates, which benefits sensor fusion for place recognition.
Object-centric Inference for Language Conditioned Placement: A Foundation Model based Approach
We focus on the task of language-conditioned object placement, in which a robot should generate placements that satisfy all the spatial relational constraints in language instructions.
UrbanGIRAFFE: Representing Urban Scenes as Compositional Generative Neural Feature Fields
Generating photorealistic images with controllable camera pose and scene contents is essential for many applications including AR/VR and simulation.
GOOD: General Optimization-based Fusion for 3D Object Detection via LiDAR-Camera Object Candidates
In this paper, we propose GOOD, a general optimization-based fusion framework that can achieve satisfying detection without training additional models and is available for any combinations of 2D and 3D detectors to improve the accuracy and robustness of 3D detection.
Open-Set Object Detection Using Classification-free Object Proposal and Instance-level Contrastive Learning
To disambiguate unknown objects and background in the first subtask, we propose to use classification-free region proposal network (CF-RPN) which estimates the objectness score of each region purely using cues from object's location and shape preventing overfitting to the training categories.
DeepRING: Learning Roto-translation Invariant Representation for LiDAR based Place Recognition
In recent years, deep learning brings improvements to place recognition by learnable feature extraction.
RING++: Roto-translation Invariant Gram for Global Localization on a Sparse Scan Map
In addition, we derive sufficient conditions of feature extractors for the representation preserving the roto-translation invariance, making RING++ a framework applicable to generic multi-channel features.
Towards Two-view 6D Object Pose Estimation: A Comparative Study on Fusion Strategy
We ascertain the Mid- Fusion approach is the best approach to restore the most precise 3D keypoints useful for object pose estimation.
DPCN++: Differentiable Phase Correlation Network for Versatile Pose Registration
Next, the rotation, scale, and translation are independently and efficiently estimated in the spectrum step-by-step using the DPC solver.
Learning A Simulation-based Visual Policy for Real-world Peg In Unseen Holes
This paper proposes a learning-based visual peg-in-hole that enables training with several shapes in simulation, and adapting to arbitrary unseen shapes in real world with minimal sim-to-real cost.
A Visual Navigation Perspective for Category-Level Object Pose Estimation
In this paper, we take a deeper look at the inference of analysis-by-synthesis from the perspective of visual navigation, and investigate what is a good navigation policy for this specific task.
Depth-Independent Depth Completion via Least Square Estimation
The depth completion task aims to complete a per-pixel dense depth map from a sparse depth map.
Translation Invariant Global Estimation of Heading Angle Using Sinogram of LiDAR Point Cloud
Global point cloud registration is an essential module for localization, of which the main difficulty exists in estimating the rotation globally without initial value.
Learning Interpretable BEV Based VIO without Deep Neural Networks
Specifically, we first adopt Unscented Kalman Filter as a differentiable layer to predict the pitch and roll, where the covariance matrices of noise are learned to filter out the noise of the IMU raw data.
Learning Stereopsis from Geometric Synthesis for 6D Object Pose Estimation
Current monocular-based 6D object pose estimation methods generally achieve less competitive results than RGBD-based methods, mostly due to the lack of 3D information.
Domain Generalization for Vision-based Driving Trajectory Generation
In this paper, we propose a domain generalization method for vision-based driving trajectory generation for autonomous vehicles in urban environments, which can be seen as a solution to extend the Invariant Risk Minimization (IRM) method in complex problems.
Improved Radar Localization on Lidar Maps Using Shared Embedding
We present a heterogeneous localization framework for solving radar global localization and pose tracking on pre-built lidar maps.
Efficient learning of goal-oriented push-grasping synergy in clutter
In this paper, a goal-conditioned hierarchical reinforcement learning formulation with high sample efficiency is proposed to learn a push-grasping policy for grasping a specific object in clutter.
Hierarchical Reinforcement Learning
Robotics
Learn to Differ: Sim2Real Small Defection Segmentation Network
In this paper, we propose the network SSDS that learns a way of distinguishing small defections between two images regardless of the context, so that the network can be trained once for all.
Collaborative Recognition of Feasible Region with Aerial and Ground Robots through DPCN
Taking the aerial robots' advantages of having large scale variance of view points of the same route which the ground robots is on, the collaboration work provides global information of road segmentation for the ground robot, thus enabling it to obtain feasible region and adjust its pose ahead of time.
Radar-to-Lidar: Heterogeneous Place Recognition via Joint Learning
Place recognition is critical for both offline mapping and online localization.
3D Point-to-Keypoint Voting Network for 6D Pose Estimation
In this paper, we propose a framework for 6D pose estimation from RGB-D data based on spatial structure characteristics of 3D keypoints.
GEM: Online Globally consistent dense elevation mapping for unstructured terrain
As a result, this deformable global dense map representation is able to keep the global consistency online.
CORAL: Colored structural representation for bi-modal place recognition
In this way, we fuse the structural features and visual features in the consistent bird-eye view frame, yielding a semantic representation, namely CORAL.
PREGAN: Pose Randomization and Estimation for Weakly Paired Image Style Translation
Utilizing the trained model under different conditions without data annotation is attractive for robot applications.
REDE: End-to-end Object 6D Pose Robust Estimation Using Differentiable Outliers Elimination
Object 6D pose estimation is a fundamental task in many applications.
Improving the generalization of network based relative pose regression: dimension reduction as a regularizer
Through experiments on real world RGBD datasets we validate the effectiveness of our design in terms of improving both generalization performance and robustness towards viewpoint change, and also show the potential of regression based visual localization networks towards challenging occasions that are difficult for geometry based visual localization methods.
DiSCO: Differentiable Scan Context with Orientation
In this paper, we propose a LiDAR-based place recognition method, named Differentiable Scan Context with Orientation (DiSCO), which simultaneously finds the scan at a similar place and estimates their relative orientation.
Imitation Learning of Hierarchical Driving Model: from Continuous Intention to Continuous Trajectory
One of the challenges to reduce the gap between the machine and the human level driving is how to endow the system with the learning capacity to deal with the coupled complexity of environments, intentions, and dynamics.
Robotics
RaLL: End-to-end Radar Localization on Lidar Map Using Differentiable Measurement Model
In this paper, we propose an end-to-end deep learning framework for Radar Localization on Lidar Map (RaLL) to bridge the gap, which not only achieves the robust radar localization but also exploits the mature lidar mapping technique, thus reducing the cost of radar mapping.
Deep Phase Correlation for End-to-End Heterogeneous Sensor Measurements Matching
The crucial step for localization is to match the current observation to the map.
Learning hierarchical behavior and motion planning for autonomous driving
To improve the tactical decision-making for learning-based driving solution, we introduce hierarchical behavior and motion planning (HBMP) to explicitly model the behavior in learning-based solution.
Weakly-Supervised Road Affordances Inference and Learning in Scenes without Traffic Signs
Road attributes understanding is extensively researched to support vehicle's action for autonomous driving, whereas current works mainly focus on urban road nets and rely much on traffic signs.
Robotics
Efficient two step optimization for large embedded deformation graph based SLAM
In this paper, we propose an approach to decouple nodes of deformation graph in large scale dense deformable SLAM and keep the estimation time to be constant.
Towards navigation without precise localization: Weakly supervised learning of goal-directed navigation cost map
Autonomous navigation based on precise localization has been widely developed in both academic research and practical applications.
Robotics
ZJUNlict Extended Team Description Paper for RoboCup 2019
For the Small Size League of RoboCup 2018, Team ZJUNLict has won the champion and therefore, this paper thoroughly described the devotion which ZJUNLict has devoted and the effort that ZJUNLict has contributed.
Robotics 68T40
LocNet: Global localization in 3D point clouds for mobile vehicles
Global localization in 3D point clouds is a challenging problem of estimating the pose of vehicles without any prior knowledge.
3D-SSD: Learning Hierarchical Features from RGB-D Images for Amodal 3D Object Detection
This paper aims at developing a faster and a more accurate solution to the amodal 3D object detection problem for indoor scenes.
