Search Results for author:
Found 96 papers, 51 papers with code
From Coarse to Fine: Robust Hierarchical Localization at Large Scale
In this paper we propose HF-Net, a hierarchical localization approach based on a monolithic CNN that simultaneously predicts local features and global descriptors for accurate 6-DoF localization.
Ranked #3 on
Visual Place Recognition
on Berlin Kudamm
Long-term Large-scale Mapping and Localization Using maplab
This paper discusses a large-scale and long-term mapping and localization scenario using the maplab open-source framework.
maplab: An Open Framework for Research in Visual-inertial Mapping and Localization
On the other hand, maplab provides the research community with a collection of multisession mapping tools that include map merging, visual-inertial batch optimization, and loop closure.
Robotics
AMZ Driverless: The Full Autonomous Racing System
This paper presents the algorithms and system architecture of an autonomous racecar.
Robotics
SegMap: 3D Segment Mapping using Data-Driven Descriptors
While current methods extract descriptors for the single task of localization, SegMap leverages a data-driven descriptor in order to extract meaningful features that can also be used for reconstructing a dense 3D map of the environment and for extracting semantic information.
SegMatch: Segment based loop-closure for 3D point clouds
We propose SegMatch, a reliable loop-closure detection algorithm based on the matching of 3D segments.
Robotics
SegMap: Segment-based mapping and localization using data-driven descriptors
We therefore present SegMap: a map representation solution for localization and mapping based on the extraction of segments in 3D point clouds.
An Efficient Sampling-based Method for Online Informative Path Planning in Unknown Environments
However, they are prone to local minima, resulting in sub-optimal trajectories, and sometimes do not reach global coverage.
Revisiting Boustrophedon Coverage Path Planning as a Generalized Traveling Salesman Problem
In this paper, we present a path planner for low-altitude terrain coverage in known environments with unmanned rotary-wing micro aerial vehicles (MAVs).
Robotics
Voxblox: Incremental 3D Euclidean Signed Distance Fields for On-Board MAV Planning
We show that we can build TSDFs faster than Octomaps, and that it is more accurate to build ESDFs out of TSDFs than occupancy maps.
Robotics
Safe Local Exploration for Replanning in Cluttered Unknown Environments for Micro-Aerial Vehicles
We perform extensive simulations to show that this system performs better than the standard approach of using an optimistic global planner, and also outperforms doing a single exploration step when the local planner is stuck.
Robotics
Sparse 3D Topological Graphs for Micro-Aerial Vehicle Planning
Micro-Aerial Vehicles (MAVs) have the advantage of moving freely in 3D space.
Robotics
Volumetric Instance-Aware Semantic Mapping and 3D Object Discovery
To autonomously navigate and plan interactions in real-world environments, robots require the ability to robustly perceive and map complex, unstructured surrounding scenes.
Robotics
VersaVIS: An Open Versatile Multi-Camera Visual-Inertial Sensor Suite
Robust and accurate pose estimation is crucial for many applications in mobile robotics.
Robotics
Volumetric Grasping Network: Real-time 6 DOF Grasp Detection in Clutter
General robot grasping in clutter requires the ability to synthesize grasps that work for previously unseen objects and that are also robust to physical interactions, such as collisions with other objects in the scene.
Robotics
C-blox: A Scalable and Consistent TSDF-based Dense Mapping Approach
Central to our approach is the representation of the environment as a collection of overlapping TSDF subvolumes.
Robotics
Comparing Task Simplifications to Learn Closed-Loop Object Picking Using Deep Reinforcement Learning
We learn closed-loop policies mapping depth camera inputs to motion commands and compare different approaches to keep the problem tractable, including reward shaping, curriculum learning and using a policy pre-trained on a task with a reduced action set to warm-start the full problem.
Robotics
Leveraging Deep Visual Descriptors for Hierarchical Efficient Localization
Many robotics applications require precise pose estimates despite operating in large and changing environments.
TSDF++: A Multi-Object Formulation for Dynamic Object Tracking and Reconstruction
The ability to simultaneously track and reconstruct multiple objects moving in the scene is of the utmost importance for robotic tasks such as autonomous navigation and interaction.
NeuralBlox: Real-Time Neural Representation Fusion for Robust Volumetric Mapping
We present a novel 3D mapping method leveraging the recent progress in neural implicit representation for 3D reconstruction.
Pixel-wise Anomaly Detection in Complex Driving Scenes
The inability of state-of-the-art semantic segmentation methods to detect anomaly instances hinders them from being deployed in safety-critical and complex applications, such as autonomous driving.
Ranked #3 on
Anomaly Detection
on Lost and Found
(using extra training data)
NavRep: Unsupervised Representations for Reinforcement Learning of Robot Navigation in Dynamic Human Environments
In this work, we design ways in which unsupervised learning can be used to assist reinforcement learning for robot navigation.
3D VSG: Long-term Semantic Scene Change Prediction through 3D Variable Scene Graphs
In this work, we formalize the task of semantic scene variability estimation and identify three main varieties of semantic scene change: changes in the position of an object, its semantic state, or the composition of a scene as a whole.
LCD -- Line Clustering and Description for Place Recognition
In this paper, we introduce a novel learning-based approach to place recognition, using RGB-D cameras and line clusters as visual and geometric features.
weedNet: Dense Semantic Weed Classification Using Multispectral Images and MAV for Smart Farming
In this paper, we present an approach for dense semantic weed classification with multispectral images collected by a micro aerial vehicle (MAV).
An informative path planning framework for UAV-based terrain monitoring
Unmanned Aerial Vehicles (UAVs) represent a new frontier in a wide range of monitoring and research applications.
Robotics
SC-Explorer: Incremental 3D Scene Completion for Safe and Efficient Exploration Mapping and Planning
We further present an informative path planning method, leveraging the capabilities of our mapping approach and a novel scene-completion-aware information gain.
3D3L: Deep Learned 3D Keypoint Detection and Description for LiDARs
Furthermore, 3D feature-based registration methods have never quite reached the robustness of 2D methods in visual SLAM.
Neural Implicit Vision-Language Feature Fields
In this work, we present a zero-shot volumetric open-vocabulary semantic scene segmentation method.
Panoptic Vision-Language Feature Fields
In this paper, we propose to the best of our knowledge the first algorithm for open-vocabulary panoptic segmentation in 3D scenes.
Depth Based Semantic Scene Completion with Position Importance Aware Loss
We propose PALNet, a novel hybrid network for SSC based on single depth.
Learning Trajectories for Visual-Inertial System Calibration via Model-based Heuristic Deep Reinforcement Learning
Visual-inertial systems rely on precise calibrations of both camera intrinsics and inter-sensor extrinsics, which typically require manually performing complex motions in front of a calibration target.
Unified Data Collection for Visual-Inertial Calibration via Deep Reinforcement Learning
Our approach models the calibration process compactly using model-free deep reinforcement learning to derive a policy that guides the motions of a robotic arm holding the sensor to efficiently collect measurements that can be used for both camera intrinsic calibration and camera-IMU extrinsic calibration.
3DSNet: Unsupervised Shape-to-Shape 3D Style Transfer
Transferring the style from one image onto another is a popular and widely studied task in computer vision.
Control of a Quadrotor with Reinforcement Learning
In this paper, we present a method to control a quadrotor with a neural network trained using reinforcement learning techniques.
Robotics
Unsupervised Continual Semantic Adaptation through Neural Rendering
Due to the mismatch between training and deployment data, adapting the model on the new scenes is often crucial to obtain good performance.
Empty Cities: Image Inpainting for a Dynamic-Object-Invariant Space
In this paper we present an end-to-end deep learning framework to turn images that show dynamic content, such as vehicles or pedestrians, into realistic static frames.
MultiPoint: Cross-spectral registration of thermal and optical aerial imagery
This model is then deployed for fast and accurate online interest point detection.
Modular Sensor Fusion for Semantic Segmentation
Sensor fusion is a fundamental process in robotic systems as it extends the perceptual range and increases robustness in real-world operations.
Hough2Map -- Iterative Event-based Hough Transform for High-Speed Railway Mapping
As a basis for a localization system we propose a complete on-board mapping pipeline able to map robust meaningful landmarks, such as poles from power lines, in the vicinity of the vehicle.
AgriColMap: Aerial-Ground Collaborative 3D Mapping for Precision Farming
The combination of aerial survey capabilities of Unmanned Aerial Vehicles with targeted intervention abilities of agricultural Unmanned Ground Vehicles can significantly improve the effectiveness of robotic systems applied to precision agriculture.
SegmentMeIfYouCan: A Benchmark for Anomaly Segmentation
State-of-the-art semantic or instance segmentation deep neural networks (DNNs) are usually trained on a closed set of semantic classes.
NavDreams: Towards Camera-Only RL Navigation Among Humans
In order to successfully solve the navigation task from only images, algorithms must be able to model the scene and its dynamics using only this channel of information.
Semi-automatic 3D Object Keypoint Annotation and Detection for the Masses
In this work, we present a full object keypoint tracking toolkit, encompassing the entire process from data collection, labeling, model learning and evaluation.
Whole-Body Control of a Mobile Manipulator using End-to-End Reinforcement Learning
Mobile manipulation is usually achieved by sequentially executing base and manipulator movements.
The Fishyscapes Benchmark: Measuring Blind Spots in Semantic Segmentation
Deep learning has enabled impressive progress in the accuracy of semantic segmentation.
Ranked #13 on
Anomaly Detection
on Fishyscapes L&F
(using extra training data)
SCIM: Simultaneous Clustering, Inference, and Mapping for Open-World Semantic Scene Understanding
In order to operate in human environments, a robot's semantic perception has to overcome open-world challenges such as novel objects and domain gaps.
Under pressure: learning-based analog gauge reading in the wild
We propose an interpretable framework for reading analog gauges that is deployable on real world robotic systems.
3D Multi-Robot Patrolling with a Two-Level Coordination Strategy
This target selection relies on a shared idleness representation and a coordination mechanism preventing topological conflicts.
Learning Camera Miscalibration Detection
Self-diagnosis and self-repair are some of the key challenges in deploying robotic platforms for long-term real-world applications.
TULIP: Transformer for Upsampling of LiDAR Point Cloud
In this paper, we propose TULIP, a new method to reconstruct high-resolution LiDAR point clouds from low-resolution LiDAR input.
Visual Place Recognition with Probabilistic Vertex Voting
We propose a novel scoring concept for visual place recognition based on nearest neighbor descriptor voting and demonstrate how the algorithm naturally emerges from the problem formulation.
Cubic Range Error Model for Stereo Vision with Illuminators
In this work, we propose that the range error is cubic in range for stereo systems with integrated illuminators.
Flexible Stereo: Constrained, Non-rigid, Wide-baseline Stereo Vision for Fixed-wing Aerial Platforms
This paper proposes a computationally efficient method to estimate the time-varying relative pose between two visual-inertial sensor rigs mounted on the flexible wings of a fixed-wing unmanned aerial vehicle (UAV).
X-View: Graph-Based Semantic Multi-View Localization
Our findings show that X-View is able to globally localize aerial-to-ground, and ground-to-ground robot data of drastically different view-points.
Towards Fully Environment-Aware UAVs: Real-Time Path Planning with Online 3D Wind Field Prediction in Complex Terrain
Overall, our initial research demonstrates the feasibility of 3D wind field prediction from a UAV and the advantages of wind-aware planning.
Meteorology-Aware Multi-Goal Path Planning for Large-Scale Inspection Missions with Long-Endurance Solar-Powered Aircraft
While this makes them promising candidates for large-scale aerial inspection missions, their structural fragility necessitates that adverse weather is avoided using appropriate path planning methods.
Dynamic Objects Segmentation for Visual Localization in Urban Environments
Yet, in highly dynamic environments, like crowded city streets, problems arise as major parts of the image can be covered by dynamic objects.
LandmarkBoost: Efficient Visual Context Classifiers for Robust Localization
We propose LandmarkBoost - an approach that, in contrast to the conventional 2D-3D matching methods, casts the search problem as a landmark classification task.
Redundant Perception and State Estimation for Reliable Autonomous Racing
In autonomous racing, vehicles operate close to the limits of handling and a sensor failure can have critical consequences.
Topomap: Topological Mapping and Navigation Based on Visual SLAM Maps
Then, we create a set of convex free-space clusters, which are the vertices of the topological map.
Robotics
Rolling Shutter Camera Calibration
Rolling Shutter (RS) cameras are used across a wide range of consumer electronic devices--from smart-phones to high-end cameras.
Robust Visual Place Recognition With Graph Kernels
A novel method for visual place recognition is introduced and evaluated, demonstrating robustness to perceptual aliasing and observation noise.
OREOS: Oriented Recognition of 3D Point Clouds in Outdoor Scenarios
We introduce a novel method for oriented place recognition with 3D LiDAR scans.
Robotics
Large-scale, real-time visual-inertial localization revisited
Our approach spans from offline model building to real-time client-side pose fusion.
Learning Densities in Feature Space for Reliable Segmentation of Indoor Scenes
Foreground objects are therefore detected as areas in an image for which the descriptors are unlikely given the background distribution.
Long-Duration Fully Autonomous Operation of Rotorcraft Unmanned Aerial Systems for Remote-Sensing Data Acquisition
Vision-based precision landing is enabled by estimating the landing pad's pose using a bundle of AprilTag fiducials configured for detection from a wide range of altitudes.
This is not what I imagined: Error Detection for Semantic Segmentation through Visual Dissimilarity
There has been a remarkable progress in the accuracy of semantic segmentation due to the capabilities of deep learning.
A Data-driven Model for Interaction-aware Pedestrian Motion Prediction in Object Cluttered Environments
This paper reports on a data-driven, interaction-aware motion prediction approach for pedestrians in environments cluttered with static obstacles.
Robotics
VIZARD: Reliable Visual Localization for Autonomous Vehicles in Urban Outdoor Environments
Changes in appearance is one of the main sources of failure in visual localization systems in outdoor environments.
Robotics
A Fully-Integrated Sensing and Control System for High-Accuracy Mobile Robotic Building Construction
We present a fully-integrated sensing and control system which enables mobile manipulator robots to execute building tasks with millimeter-scale accuracy on building construction sites.
End-to-End Velocity Estimation For Autonomous Racing
Velocity estimation plays a central role in driverless vehicles, but standard and affordable methods struggle to cope with extreme scenarios like aggressive maneuvers due to the presence of high sideslip.
Go Fetch: Mobile Manipulation in Unstructured Environments
With humankind facing new and increasingly large-scale challenges in the medical and domestic spheres, automation of the service sector carries a tremendous potential for improved efficiency, quality, and safety of operations.
Deep Learning-based Human Detection for UAVs with Optical and Infrared Cameras: System and Experiments
In this paper, we present our deep learning-based human detection system that uses optical (RGB) and long-wave infrared (LWIR) cameras to detect, track, localize, and re-identify humans from UAVs flying at high altitude.
Deep UAV Localization with Reference View Rendering
This paper presents a framework for the localization of Unmanned Aerial Vehicles (UAVs) in unstructured environments with the help of deep learning.
IDOL: A Framework for IMU-DVS Odometry using Lines
The method's front-end extracts event clusters that belong to line segments in the environment whereas the back-end estimates the system's trajectory alongside the lines' 3D position by minimizing point-to-line distances between individual events and the lines' projection in the image space.
Robotics
Out-of-Distribution Detection for Automotive Perception
A mechanism to detect OOD samples is important for safety-critical applications, such as automotive perception, to trigger a safe fallback mode.
Freetures: Localization in Signed Distance Function Maps
Localization of a robotic system within a previously mapped environment is important for reducing estimation drift and for reusing previously built maps.
Robotics
The Hidden Uncertainty in a Neural Networks Activations
We find that this leads to improved OOD detection of epistemic uncertainty at the cost of ambiguous calibration close to the data distribution.
Active Interaction Force Control for Contact-Based Inspection with a Fully Actuated Aerial Vehicle
A fully actuated and omnidirectional tilt-rotor aerial system is used to show capabilities of the control and planning methods.
Robotics
Active Model Learning using Informative Trajectories for Improved Closed-Loop Control on Real Robots
However, the efficient and effective data collection for such a data-driven system on real robots is still an open challenge.
Robotics Systems and Control Systems and Control
Points2Vec: Unsupervised Object-level Feature Learning from Point Clouds
This, despite the fact that the physical 3D spaces have a similar semantic structure to bodies of text: words are surrounded by words that are semantically related, just like objects are surrounded by other objects that are similar in concept and usage.
SD-6DoF-ICLK: Sparse and Deep Inverse Compositional Lucas-Kanade Algorithm on SE(3)
This paper introduces SD-6DoF-ICLK, a learning-based Inverse Compositional Lucas-Kanade (ICLK) pipeline that uses sparse depth information to optimize the relative pose that best aligns two images on SE(3).
Fast Image-Anomaly Mitigation for Autonomous Mobile Robots
Camera anomalies like rain or dust can severelydegrade image quality and its related tasks, such as localizationand segmentation.
Superquadric Object Representation for Optimization-based Semantic SLAM
Introducing semantically meaningful objects to visual Simultaneous Localization And Mapping (SLAM) has the potential to improve both the accuracy and reliability of pose estimates, especially in challenging scenarios with significant view-point and appearance changes.
See Yourself in Others: Attending Multiple Tasks for Own Failure Detection
The proposed framework infers task failures by evaluating the individual prediction, across multiple visual perception tasks for different regions in an image.
Witness Autoencoder: Shaping the Latent Space with Witness Complexes
We present a Witness Autoencoder (W-AE) – an autoencoder that captures geodesic distances of the data in the latent space.
Descriptellation: Deep Learned Constellation Descriptors
However, handcrafted topological descriptors are hard to tune and not robust to environmental noise, drastic perspective changes, object occlusion or misdetections.
Sampling-free obstacle gradients and reactive planning in Neural Radiance Fields (NeRF)
This work investigates the use of Neural implicit representations, specifically Neural Radiance Fields (NeRF), for geometrical queries and motion planning.
Learning Variable Impedance Control for Aerial Sliding on Uneven Heterogeneous Surfaces by Proprioceptive and Tactile Sensing
The recent development of novel aerial vehicles capable of physically interacting with the environment leads to new applications such as contact-based inspection.
Baking in the Feature: Accelerating Volumetric Segmentation by Rendering Feature Maps
Methods have recently been proposed that densely segment 3D volumes into classes using only color images and expert supervision in the form of sparse semantically annotated pixels.
Local and Global Information in Obstacle Detection on Railway Tracks
We evaluate our method on a custom dataset featuring railway images with artificially augmented obstacles.
ISAR: A Benchmark for Single- and Few-Shot Object Instance Segmentation and Re-Identification
To build spatial AI systems that can quickly be taught about new objects, we need to effectively solve the problem of single-shot object detection, instance segmentation and re-identification.
WindSeer: Real-time volumetric wind prediction over complex terrain aboard a small UAV
Real-time high-resolution wind predictions are beneficial for various applications including safe manned and unmanned aviation.
VIRUS-NeRF -- Vision, InfraRed and UltraSonic based Neural Radiance Fields
Building upon Instant Neural Graphics Primitives with a Multiresolution Hash Encoding (Instant-NGP), VIRUS-NeRF incorporates depth measurements from ultrasonic and infrared sensors and utilizes them to update the occupancy grid used for ray marching.
Zero123-6D: Zero-shot Novel View Synthesis for RGB Category-level 6D Pose Estimation
Estimating the pose of objects through vision is essential to make robotic platforms interact with the environment.
