652 citations · 7k across the 163 of their papers we have counts for
30 papers · 1 filter
Multi-Resolution Elevation Mapping and Safe Landing Site Detection with Applications to Planetary Rotorcraft
Pascal Schoppmann, Pedro F. Proença, Jeff Delaune +5
In this paper, we propose a resource-efficient approach to provide an autonomous UAV with an on-board perception method to detect safe, hazard-free landing sites during flights ove…
Autonomous Teamed Exploration of Subterranean Environments using Legged and Aerial Robots
Mihir Kulkarni, Mihir Dharmadhikari, Marco Tranzatto +10
This paper presents a novel strategy for autonomous teamed exploration of subterranean environments using legged and aerial robots. Tailored to the fact that subterranean settings,…
Continual Adaptation of Semantic Segmentation using Complementary 2D-3D Data Representations
Jonas Frey, Hermann Blum, Francesco Milano +2
Semantic segmentation networks are usually pre-trained once and not updated during deployment. As a consequence, misclassifications commonly occur if the distribution of the traini…
NeuralBlox: Real-Time Neural Representation Fusion for Robust Volumetric Mapping
Stefan Lionar, Lukas Schmid, Cesar Cadena +2
We present a novel 3D mapping method leveraging the recent progress in neural implicit representation for 3D reconstruction. Most existing state-of-the-art neural implicit represen…
See Yourself in Others: Attending Multiple Tasks for Own Failure Detection
Boyang Sun, Jiaxu Xing, Hermann Blum +2
Autonomous robots deal with unexpected scenarios in real environments. Given input images, various visual perception tasks can be performed, e.g., semantic segmentation, depth esti…
Unified Data Collection for Visual-Inertial Calibration via Deep Reinforcement Learning
Yunke Ao, Le Chen, Florian Tschopp +3
Visual-inertial sensors have a wide range of applications in robotics. However, good performance often requires different sophisticated motion routines to accurately calibrate came…
Superquadric Object Representation for Optimization-based Semantic SLAM
Florian Tschopp, Juan Nieto, Roland Siegwart +1
Introducing semantically meaningful objects to visual Simultaneous Localization And Mapping (SLAM) has the potential to improve both the accuracy and reliability of pose estimates,…
Fast Image-Anomaly Mitigation for Autonomous Mobile Robots
Gianmario Fumagalli, Yannick Huber, Marcin Dymczyk +2
Camera anomalies like rain or dust can severelydegrade image quality and its related tasks, such as localizationand segmentation. In this work we address this importantissue by imp…
Panoptic Multi-TSDFs: a Flexible Representation for Online Multi-resolution Volumetric Mapping and Long-term Dynamic Scene Consistency
Lukas Schmid, Jeffrey Delmerico, Johannes Schönberger +4
For robotic interaction in environments shared with other agents, access to volumetric and semantic maps of the scene is crucial. However, such environments are inevitably subject…
Modelling and Estimation of Human Walking Gait for Physical Human-Robot Interaction
Yash Vyas, Mike Allenspach, Christian Lanegger +2
An approach to model and estimate human walking kinematics in real-time for Physical Human-Robot Interaction is presented. The human gait velocity along the forward and vertical di…
SemSegMap- 3D Segment-Based Semantic Localization
Andrei Cramariuc, Florian Tschopp, Nikhilesh Alatur +6
Localization is an essential task for mobile autonomous robotic systems that want to use pre-existing maps or create new ones in the context of SLAM. Today, many robotic platforms…
Under the Sand: Navigation and Localization of a Micro Aerial Vehicle for Landmine Detection with Ground Penetrating Synthetic Aperture Radar
Rik Bähnemann, Nicholas Lawrance, Lucas Streichenberg +5
Ground penetrating radar mounted on micro aerial vehicle (MAV) is a promising tool to assist humanitarian landmine clearance. However, the quality of synthetic aperture radar image…
TSDF++: A Multi-Object Formulation for Dynamic Object Tracking and Reconstruction
Margarita Grinvald, Federico Tombari, Roland Siegwart +1
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…
Self-Improving Semantic Perception for Indoor Localisation
Hermann Blum, Francesco Milano, René Zurbrügg +3
We propose a novel robotic system that can improve its perception during deployment. Contrary to the established approach of learning semantics from large datasets and deploying fi…
Crowd against the machine: A simulation-based benchmark tool to evaluate and compare robot capabilities to navigate a human crowd
Fabien Grzeskowiak, David Gonon, Daniel Dugas +7
The evaluation of robot capabilities to navigate human crowds is essential to conceive new robots intended to operate in public spaces. This paper initiates the development of a be…
Spherical Multi-Modal Place Recognition for Heterogeneous Sensor Systems
Lukas Bernreiter, Lionel Ott, Juan Nieto +2
In this paper, we propose a robust end-to-end multi-modal pipeline for place recognition where the sensor systems can differ from the map building to the query. Our approach operat…
3D3L: Deep Learned 3D Keypoint Detection and Description for LiDARs
Dominic Streiff, Lukas Bernreiter, Florian Tschopp +2
With the advent of powerful, light-weight 3D LiDARs, they have become the hearth of many navigation and SLAM algorithms on various autonomous systems. Pointcloud registration metho…
CalQNet -- Detection of Calibration Quality for Life-Long Stereo Camera Setups
Jiapeng Zhong, Zheyu Ye, Andrei Cramariuc +4
Many mobile robotic platforms rely on an accurate knowledge of the extrinsic calibration parameters, especially systems performing visual stereo matching. Although a number of accu…
Dynamic Object Aware LiDAR SLAM based on Automatic Generation of Training Data
Patrick Pfreundschuh, Hubertus Franciscus Cornelis Hendrikx, Victor Reijgwart +3
Highly dynamic environments, with moving objects such as cars or humans, can pose a performance challenge for LiDAR SLAM systems that assume largely static scenes. To overcome this…
SegmentMeIfYouCan: A Benchmark for Anomaly Segmentation
Robin Chan, Krzysztof Lis, Svenja Uhlemeyer +6
State-of-the-art semantic or instance segmentation deep neural networks (DNNs) are usually trained on a closed set of semantic classes. As such, they are ill-equipped to handle pre…
Dynamic-Aware Autonomous Exploration in Populated Environments
Valentina Cavinato, Thomas Eppenberger, Dina Youakim +2
Autonomous exploration allows mobile robots to navigate in initially unknown territories in order to build complete representations of the environments. In many real-life applicati…
SD-6DoF-ICLK: Sparse and Deep Inverse Compositional Lucas-Kanade Algorithm on SE(3)
Timo Hinzmann, Roland Siegwart
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 a…
Online Informative Path Planning for Active Information Gathering of a 3D Surface
Hai Zhu, Jen Jen Chung, Nicholas R. J. Lawrance +2
This paper presents an online informative path planning approach for active information gathering on three-dimensional surfaces using aerial robots. Most existing works on surface…
Pixel-wise Anomaly Detection in Complex Driving Scenes
Giancarlo Di Biase, Hermann Blum, Roland Siegwart +1
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 au…