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Model Predictive Control for Micro Aerial Vehicles: A Survey
(2021)2021 European Control Conference (ECC)This paper presents a review of the design and application of model predictive control strategies for Micro Aerial Vehicles and specifically multirotor configurations such as quadrotors. The diverse set of works in the domain is organized based on the control law being optimized over linear or nonlinear dynamics, the integration of state and input constraints, possible fault-tolerant design, if reinforcement learning methods have been ...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 -
Dynamic Object Aware LiDAR SLAM based on Automatic Generation of Training Data
(2021)2021 IEEE International Conference on Robotics and Automation (ICRA)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 challenge and support the deployment of robots in real world scenarios, we propose a complete solution for a dynamic object aware LiDAR SLAM algorithm. This is achieved by leveraging a real-time capable neural network that can detect dynamic objects, thus ...Conference Paper -
Learning Common and Transferable Feature Representations for Multi-Modal Data
(2020)2020 IEEE Intelligent Vehicles Symposium (IV)LiDAR sensors are crucial in automotive perception for accurate object detection. However, LiDAR data is hard to interpret for humans and consequently time-consuming to label. Whereas camera data is easy interpretable and thus, comparably simpler to label. Within this work we present a transductive transfer learning approach to transfer the knowledge for the object detection task from images to point cloud data. We propose a multi-modal ...Conference Paper -
Trajectory Tracking Nonlinear Model Predictive Control for an Overactuated MAV
(2020)2020 IEEE International Conference on Robotics and Automation (ICRA)This work presents a method to control omnidirectional micro aerial vehicles (OMAVs) for the tracking of 6-DoF trajectories in free space. A rigid body model based approach is applied in a receding horizon fashion to generate optimal wrench commands that can be constrained to meet limits given by the mechanical design and actuators of the platform. Allocation of optimal actuator commands is performed in a separate step. A disturbance ...Conference Paper -
Hybrid Topological and 3D Dense Mapping through Autonomous Exploration for Large Indoor Environments
(2020)2020 IEEE International Conference on Robotics and Automation (ICRA)Robots require a detailed understanding of the 3D structure of the environment for autonomous navigation and path planning. A popular approach is to represent the environment using metric, dense 3D maps such as 3D occupancy grids. However, in large environments the computational power required for most state-of-the-art 3D dense mapping systems is compromising precision and real-time capability. In this work, we propose a novel mapping ...Conference Paper -
3D VSG: Long-term Semantic Scene Change Prediction through 3D Variable Scene Graphs
(2023)2023 IEEE International Conference on Robotics and Automation (ICRA)Numerous applications require robots to operate in environments shared with other agents, such as humans or other robots. However, such shared scenes are typically subject to different kinds of long-term semantic scene changes. The ability to model and predict such changes is thus crucial for robot autonomy. In this work, we formalize the task of semantic scene variability estimation and identify three main varieties of semantic scene ...Conference Paper -
SegmentMeIfYouCan: A Benchmark for Anomaly Segmentation
(2021)Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1 (NeurIPS Datasets and Benchmarks 2021)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 previously-unseen objects. However, detecting and localizing such objects is crucial for safety-critical applications such as perception for automated driving, especially if they appear on the road ahead. While some methods have tackled the tasks of anomalous or ...Conference Paper -
Design Optimization ofaFour-Bar Leg Linkage foraLegged-Wheeled Balancing Robot
(2023)Lecture Notes in Networks and Systems ~ Robotics in Natural SettingsBalancing legged-wheeled robots have gained popularity in recent years due to their locomotive efficiency while still being able to conquer rough terrain and obstacles. Furthermore, as this type of robot maintains ground contact with its wheels for most of the time, passive gravity compensation mechanisms can greatly minimize power consumption. Various designs with different leg configurations have emerged, whereby a 1-DOF mechanism per ...Conference Paper -
SphNet: A Spherical Network for Semantic Pointcloud Segmentation
(2023)2023 IEEE International Conference on Robotics and Automation (ICRA)Semantic segmentation for robotic systems can enable a wide range of applications, from self-driving cars and augmented reality systems to domestic robots. We argue that a spherical representation is a natural one for egocentric pointclouds. Thus, in this work, we present a novel framework exploiting such a representation of LiDAR pointclouds for the task of semantic segmentation. Our approach is based on a spherical convolutional neural ...Conference Paper