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Global Localization in Meshes
(2021)ISARC Proceedings ~ Proceedings of the 38th International Symposium on Automation and Robotics in Construction (ISARC)Safely waking up a robot at an unknown location and subsequent autonomous operation are key requirements for on-site construction robots. In this regard, single-shot global localization in a known map is a challenging problem due to incomplete observations of the environment and sensor obstructions by unmapped clutter. In this work, we address global localization of sparse multi-beam LiDAR measurements in a 3D mesh building model, a typical ...Conference Paper -
Precise Robot Localization in Architectural 3D Plans
(2021)ISARC Proceedings ~ Proceedings of the 38th International Symposium on Automation and Robotics in Construction (ISARC)This paper presents a localization system for mobile robots enabling precise localization in inaccurate building models. The approach leverages local referencing to counteract inherent deviations between as-planned and as-built data for locally accurate registration. We further fuse a novel camera-based robust outlier detector with LiDAR data to reject a wide range of outlier measurements from clutter, dynamic objects, and sensor failures. ...Conference Paper -
Dipper: A Dynamically Transitioning Aerial-Aquatic Unmanned Vehicle
(2021)Proceedings of Robotics: Science and Systems XVIIThe locomotion for many modern robotic systems is optimized for a single target domain - aerial, surface or underwater. In this work, we address the challenge of developing a robotic system capable of controlled motion in air and underwater. Further, we explore the particular challenge of dynamic transitions between air and water. We propose Dipper, an aerial-aquatic hybrid vehicle. Dipper is a lightweight fixed-wing unmanned aerial vehicle ...Conference Paper -
Spherical Multi-Modal Place Recognition for Heterogeneous Sensor Systems
(2021)2021 IEEE International Conference on Robotics and Automation (ICRA)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 operates directly on images and LiDAR scans without requiring any local feature extraction modules. By projecting the sensor data onto the unit sphere, we learn a multi-modal descriptor of partially overlapping scenes using a spherical convolutional neural network. ...Conference Paper -
Dynamic-Aware Autonomous Exploration in Populated Environments
(2021)2021 IEEE International Conference on Robotics and Automation (ICRA)Autonomous exploration allows mobile robots to navigate in initially unknown territories in order to build complete representations of the environments. In many real-life applications, environments often contain dynamic obstacles which can compromise the exploration process by temporarily blocking passages, narrow paths, exits or entrances to other areas yet to be explored. In this work, we formulate a novel exploration strategy capable ...Conference Paper -
3D3L: Deep Learned 3D Keypoint Detection and Description for LiDARs
(2021)2021 IEEE International Conference on Robotics and Automation (ICRA)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 methods working with unstructured pointclouds such as ICP are often computationally expensive or require a good initial guess. Furthermore, 3D feature-based registration methods have never quite reached the robustness of 2D methods in visual SLAM. With the continuously ...Conference Paper -
A Photorealistic Terrain Simulation Pipeline for Unstructured Outdoor Environments
(2021)2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)Suitable datasets are an integral part of robotics research, especially for training neural networks in robot perception. However, in many domains, suitable real-world data are scarce and cannot be easily obtained. This problem is especially prevalent for unstructured outdoor environments, in particular, planetary ones. Recent advances in photorealistic simulations help researchers to simulate close-to-real data in many domains. Yet, there ...Conference Paper -
Modelling and Estimation of Human Walking Gait for Physical Human-Robot Interaction
(2021)2021 Aerial Robotic Systems Physically Interacting with the Environment (AIRPHARO)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 direction of motion is modelled according to the Yoyo-model. We designed an Extended Kalman Filter (EKF) algorithm to estimate the frequency, bias and trigonometric state of a biased sinusoidal signal, from which the kinematic parameters of the Yoyo-model can be ...Conference Paper -
RoBoa: Construction and evaluation of a steerable vine robot for search and rescue applications
(2021)2021 IEEE 4th International Conference on Soft Robotics (RoboSoft)RoBoa is a vine-like search and rescue robot that can explore narrow and cluttered environments such as destroyed buildings. The robot assists rescue teams in finding and communicating with trapped people. It employs the principle of vine robots for locomotion, everting the tip of its tube to move forward. Inside the tube, pneumatic actuators enable lateral movement. The head carries sensors and is mounted outside at the tip of the tube. ...Conference Paper -
Airborne particle classification in LiDAR point clouds using deep learning
(2021)Springer Proceedings in Advanced Robotics ~ Field and Service RoboticsLiDAR sensors have been very popular in robotics due to their ability to provide accurate range measurements and their robustness to lighting conditions. However, their sensitivity to airborne particles such as dust or fog can lead to perception algorithm failures (e.g., the detection of false obstacles by field robots). In this work, we address this problem by proposing methods to classify airborne particles in LiDAR data. We propose and ...Conference Paper