Search Results for author:
Found 38 papers, 16 papers with code
Quantifying Epistemic Uncertainty in Absolute Pose Regression
Absolute pose regression offers a solution to this task by training a neural network, directly regressing the camera pose from image features.
Information Gain Is Not All You Need
Autonomous exploration in mobile robotics often involves a trade-off between two objectives: maximizing environmental coverage and minimizing the total path length.
HiMo: High-Speed Objects Motion Compensation in Point Clouds
To address this challenge, we introduce HiMo, a pipeline that repurposes scene flow estimation for non-ego motion compensation, correcting the representation of dynamic objects in point clouds.
SSF: Sparse Long-Range Scene Flow for Autonomous Driving
To address this, we propose a sparse feature fusion scheme, that augments the feature maps with virtual voxels at missing locations.
Conditional Variational Autoencoders for Probabilistic Pose Regression
Robots rely on visual relocalization to estimate their pose from camera images when they lose track.
ExelMap: Explainable Element-based HD-Map Change Detection and Update
Acquisition and maintenance are central problems in deploying high-definition (HD) maps for autonomous driving, with two lines of research prevalent in current literature: Online HD map generation and HD map change detection.
SeFlow: A Self-Supervised Scene Flow Method in Autonomous Driving
Scene flow estimation predicts the 3D motion at each point in successive LiDAR scans.
Ranked #1 on
Self-supervised Scene Flow Estimation
on Argoverse 2
Beyond the Frontier: Predicting Unseen Walls from Occupancy Grids by Learning from Floor Plans
In this paper, we tackle the challenge of predicting the unseen walls of a partially observed environment as a set of 2D line segments, conditioned on occupancy grids integrated along the trajectory of a 360{\deg} LIDAR sensor.
BeautyMap: Binary-Encoded Adaptable Ground Matrix for Dynamic Points Removal in Global Maps
In response, we present BeautyMap to efficiently remove the dynamic points while retaining static features for high-fidelity global maps.
Neural Graph Mapping for Dense SLAM with Efficient Loop Closure
Existing neural field-based SLAM methods typically employ a single monolithic field as their scene representation.
Addressing Data Annotation Challenges in Multiple Sensors: A Solution for Scania Collected Datasets
However, highly dynamic objects pose a unique challenge, as they can appear at different timestamps in each sensor's data.
UADA3D: Unsupervised Adversarial Domain Adaptation for 3D Object Detection with Sparse LiDAR and Large Domain Gaps
In this study, we address a gap in existing unsupervised domain adaptation approaches on LiDAR-based 3D object detection, which have predominantly concentrated on adapting between established, high-density autonomous driving datasets.
MCD: Diverse Large-Scale Multi-Campus Dataset for Robot Perception
Perception plays a crucial role in various robot applications.
DUFOMap: Efficient Dynamic Awareness Mapping
The dynamic nature of the real world is one of the main challenges in robotics.
DeFlow: Decoder of Scene Flow Network in Autonomous Driving
Scene flow estimation determines a scene's 3D motion field, by predicting the motion of points in the scene, especially for aiding tasks in autonomous driving.
Ranked #1 on
Scene Flow Estimation
on Argoverse 2
Towards Long-Range 3D Object Detection for Autonomous Vehicles
But most current state of the art LiDAR based methods are range limited due to sparsity at long range, which generates a form of domain gap between points closer to and farther away from the ego vehicle.
A Dynamic Points Removal Benchmark in Point Cloud Maps
In the field of robotics, the point cloud has become an essential map representation.
Towards a Robust Sensor Fusion Step for 3D Object Detection on Corrupted Data
Multimodal sensor fusion methods for 3D object detection have been revolutionizing the autonomous driving research field.
RGB-D-Based Categorical Object Pose and Shape Estimation: Methods, Datasets, and Evaluation
Recently, various methods for 6D pose and shape estimation of objects at a per-category level have been proposed.
A Probabilistic Framework for Visual Localization in Ambiguous Scenes
Visual localization allows autonomous robots to relocalize when losing track of their pose by matching their current observation with past ones.
SDFEst: Categorical Pose and Shape Estimation of Objects from RGB-D using Signed Distance Fields
Rich geometric understanding of the world is an important component of many robotic applications such as planning and manipulation.
SDF-based RGB-D Camera Tracking in Neural Scene Representations
We consider the problem of tracking the 6D pose of a moving RGB-D camera in a neural scene representation.
FloorGenT: Generative Vector Graphic Model of Floor Plans for Robotics
Floor plans are the basis of reasoning in and communicating about indoor environments.
On the Evaluation of RGB-D-based Categorical Pose and Shape Estimation
Recently, various methods for 6D pose and shape estimation of objects have been proposed.
UFOMap: An Efficient Probabilistic 3D Mapping Framework That Embraces the Unknown
In applications where the environment is unknown a-priori, or where only a part of the environment is known, it is important that the 3D model can handle the unknown space efficiently.
Robotics
Self-Supervised 3D Keypoint Learning for Ego-motion Estimation
Detecting and matching robust viewpoint-invariant keypoints is critical for visual SLAM and Structure-from-Motion.
Skew-Explore: Learn faster in continuous spaces with sparse rewards
In this work, we introduce an exploration approach based on maximizing the entropy of the visited states while learning a goal-conditioned policy.
Adversarial Feature Training for Generalizable Robotic Visuomotor Control
Deep reinforcement learning (RL) has enabled training action-selection policies, end-to-end, by learning a function which maps image pixels to action outputs.
Knowledge is Never Enough: Towards Web Aided Deep Open World Recognition
While today's robots are able to perform sophisticated tasks, they can only act on objects they have been trained to recognize.
Sparse2Dense: From direct sparse odometry to dense 3D reconstruction
In this paper, we proposed a new deep learning based dense monocular SLAM method.
GCNv2: Efficient Correspondence Prediction for Real-Time SLAM
In this paper, we present a deep learning-based network, GCNv2, for generation of keypoints and descriptors.
Meta-Learning for Multi-objective Reinforcement Learning
Multi-objective reinforcement learning (MORL) is the generalization of standard reinforcement learning (RL) approaches to solve sequential decision making problems that consist of several, possibly conflicting, objectives.
Kitting in the Wild through Online Domain Adaptation
This novel dataset allows for testing the robustness of robot visual recognition algorithms to a series of different domain shifts both in isolation and unified.
Deep Reinforcement Learning to Acquire Navigation Skills for Wheel-Legged Robots in Complex Environments
Mobile robot navigation in complex and dynamic environments is a challenging but important problem.
Fusing Saliency Maps with Region Proposals for Unsupervised Object Localization
These regions are further refined based on the overlap and similarity ratios.
Detection and Tracking of General Movable Objects in Large 3D Maps
This paper studies the problem of detection and tracking of general objects with long-term dynamics, observed by a mobile robot moving in a large environment.
Unsupervised Object Discovery and Segmentation of RGBD-images
In this paper we introduce a system for unsupervised object discovery and segmentation of RGBD-images.
Adaptive Cost Function for Pointcloud Registration
In this paper we introduce an adaptive cost function for pointcloud registration.
