检索结果-内蒙古大学图书馆

Real-Time Monocular visual Odometry for On-Road Vehicles with 1-Point RANSAC

学校读者我要写书评

暂无评论

Real-Time Monocular Visual Odometry for On-Road Vehicles wit...

Dynamic Maps 2010

作者： Scaramuzza, Davide Fraundorfer, Friedrich Siegwart, Roland Autonomous Systems Lab ETH Zurich Switzerland Computer Vision and Geometry Group ETH Zurich Switzerland

This paper presents a system capable of recovering the trajectory of a vehicle from the video input of a single camera at a very high frame-rate. The overall frame-rate is limited only by the feature extraction process, as the outlier removal and the motion estimation steps take less than 1 millisecond with a normal laptop computer. The algorithm relies on a novel way of removing the outliers of the feature matching process. We show that by exploiting the nonholonomic constraints of wheeled vehicles it is possible to use a restrictive motion model which allows us to parameterize the motion with only 1 feature correspondence. Using a single feature correspondence for motion estimation is the lowest model parameterization possible and results in the most efficient algorithms for removing outliers. Here we present two methods for outlier removal. One based on RANSAC and the other one based on histogram voting. We demonstrate the approach using an omnidirectional camera placed on a vehicle during a peak time tour in the city of zurich. We show that the proposed algorithm is able to cope with the large amount of clutter of the city (other moving cars, buses, trams, pedestrians, sudden stops of the vehicle, etc.). Using the proposed approach, we cover one of the longest trajectories ever reported in real-time from a single omnidirectional camera and in cluttered urban scenes, up to 3 kilometers. © 2011 Dagstuhl Seminar Proceedings. All rights reserved.

关键词： Motion estimation

Learning Deep Sensorimotor Policies for vision-based Autonomous Drone Racing

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Fu, Jiawei Song, Yunlong Wu, Yan Yu, Fisher Scaramuzza, Davide Robotics and Perception Group Department of Informatics University of Zurich Department of Neuroinformatics University of Zurich ETH Zurich Switzerland Visual Intelligence and Systems Group in the Computer Vision Lab at ETH Zurich Switzerland

unstructured environments, enabling various real-world applications. However, the lack of effective vision-based algorithms has been a stumbling block to achieving this goal. Existing systems often require hand-engineered components for state estimation, planning, and control. Such a sequential design involves laborious tuning, human heuristics, and compounding delays and errors. This paper tackles the vision-based autonomous-drone-racing problem by learning deep sensorimotor policies. We use contrastive learning to extract robust feature representations from the input images and leverage a two-stage learning-by-cheating framework for training a neural network policy. The resulting policy directly infers control commands with feature representations learned from raw images, forgoing the need for globally-consistent state estimation, trajectory planning, and handcrafted control design. Our experimental results indicate that our vision-based policy can achieve the same level of racing performance as the state-based policy while being robust against different visual disturbances and distractors. We believe this work serves as a stepping-stone toward developing intelligent vision-based autonomous systems that control the drone purely from image inputs, like human pilots. Copyright © 2022, The Authors. All rights reserved.

关键词： Drones

Learning Deep Sensorimotor Policies for vision-Based Autonomous Drone Racing

学校读者我要写书评

暂无评论

Learning Deep Sensorimotor Policies for Vision-Based Autonom...

IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Jiawei Fu Yunlong Song Yan Wu Fisher Yu Davide Scaramuzza Department of Informatics Robotics and Perception Group University of Zurich Switzerland Department of Neuroinformatics University of Zurich and ETH Zurich Switzerland Visual Intelligence and Systems Group in the Computer Vision Lab ETH Zurich

The development of effective vision-based algorithms has been a significant challenge in achieving autonomous drones, which promise to offer immense potential for many real-world applications. This paper investigates learning deep sensorimotor policies for vision-based drone racing, which is a particularly demanding setting for testing the limits of an algorithm. Our method combines feature representation learning to extract task-relevant feature representations from high-dimensional image inputs with a learning-by-cheating framework to train a deep sensorimotor policy for vision-based drone racing. This approach eliminates the need for globally-consistent state estimation, trajectory planning, and handcrafted control design, allowing the policy to directly infer control commands from raw images, similar to human pilots. We conduct experiments using a realistic simulator and show that our vision-based policy can achieve state-of-the-art racing performance while being robust against unseen visual disturbances. Our study suggests that consistent feature embeddings are essential for achieving robust control performance in the presence of visual disturbances. The key to acquiring consistent feature embeddings is utilizing contrastive learning along with data augmentation. Video: https://***/AX_fcnW9yqE

关键词：

Object Finding in Cluttered Scenes Using Interactive Perception

学校读者我要写书评

暂无评论

Object Finding in Cluttered Scenes Using Interactive Percept...

IEEE International Conference on Robotics and Automation (ICRA)

作者： Tonci Novkovic Remi Pautrat Fadri Furrer Michel Breyer Roland Siegwart Juan Nieto Autonomous Systems Lab ETH Zurich Switzerland Computer Vision and Geometry Group ETH Zurich Switzerland

ISBN: (数字)9781728173955

ISBN: (纸本)9781728173962

Object finding in clutter is a skill that requires perception of the environment and in many cases physical interaction. In robotics, interactive perception defines a set of algorithms that leverage actions to improve the perception of the environment, and vice versa use perception to guide the next action. Scene interactions are difficult to model, therefore, most of the current systems use predefined heuristics. This limits their ability to efficiently search for the target object in a complex environment. In order to remove heuristics and the need for explicit models of the interactions, in this work we propose a reinforcement learning based active and interactive perception system for scene exploration and object search. We evaluate our work both in simulated and in real-world experiments using a robotic manipulator equipped with an RGB and a depth camera, and compare our system to two baselines. The results indicate that our approach, trained in simulation only, transfers smoothly to reality and can solve the object finding task efficiently and with more than 88% success rate.

关键词： Cameras Task analysis Robot sensing systems Search problems Clutter Detectors

Object finding in cluttered scenes using interactive perception

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Novkovic, Tonci Pautrat, Remi Furrer, Fadri Breyer, Michel Siegwart, Roland Nieto, Juan Autonomous Systems Lab ETH Zurich8092 Switzerland Computer Vision and Geometry Group ETH Zurich8092 Switzerland

Object finding in clutter is a skill that requires both perception of the environment and in many cases physical interaction. In robotics, interactive perception defines a set of algorithms that leverage actions to improve the perception of the environment, and vice versa use perception to guide the next action. Scene interactions are difficult to model, therefore, most of the current systems use predefined heuristics. This limits their ability to efficiently search for the target object in a complex environment. In order to remove heuristics and the need for explicit models of the interactions, in this work we propose a reinforcement learning based active and interactive perception system for scene exploration and object search. We evaluate our work both in simulated and in real world experiments using a robotic manipulator equipped with an RGB and a depth camera, and compared our system to two baselines. The results indicate that our approach, trained in simulation only, transfers smoothly to reality and can solve the object finding task efficiently and with more than 90% success rate. Copyright © 2019, The Authors. All rights reserved.

关键词： Robotics

Incremental object database: Building 3D models from multiple partial observations

学校读者我要写书评

暂无评论

arXiv 2018年

作者： Furrer, Fadri Novkovic, Tonci Fehr, Marius Gawel, Abel Grinvald, Margarita Sattler, Torsten Siegwart, Roland Nieto, Juan Autonomous Systems Lab ETH Zurich8092 Switzerland Computer Vision Group Deparment of Computer Science ETH Zurich8092 Switzerland

Collecting 3D object datasets involves a large amount of manual work and is time consuming. Getting complete models of objects either requires a 3D scanner that covers all the surfaces of an object or one needs to rotate it to completely observe it. We present a system that incrementally builds a database of objects as a mobile agent traverses a scene. Our approach requires no prior knowledge of the shapes present in the scene. Object-like segments are extracted from a global segmentation map, which is built online using the input of segmented RGB-D images. These segments are stored in a database, matched among each other, and merged with other previously observed instances. This allows us to create and improve object models on the fly and to use these merged models to reconstruct also unobserved parts of the scene. The database contains each (potentially merged) object model only once, together with a set of poses where it was observed. We evaluate our pipeline with one public dataset, and on a newly created Google Tango dataset containing four indoor scenes with some of the objects appearing multiple times, both within and across scenes. Copyright © 2018, The Authors. All rights reserved.

关键词： Mobile agents

Reinforced imitation: Sample efficient deep reinforcement learning for map-less navigation by leveraging prior demonstrations

学校读者我要写书评

暂无评论

arXiv 2018年

作者： Pfeiffer, M. Shukla, S. Turchetta, M. Cadena, C. Krause, A. Siegwart, R. Nieto, J. Autonomous Systems Lab Zurich Switzerland Computer Vision Lab Zurich Switzerland Learning & Adaptive Systems Group Zurich Switzerland Max Planck ETH Center for Learning Systems ETH Zurich Zurich Switzerland

This work presents a case study of a learning-based approach for target driven map-less navigation. The underlying navigation model is an end-to-end neural network which is trained using a combination of expert demonstrations, imitation learning (IL) and reinforcement learning (RL). While RL and IL suffer from a large sample complexity and the distribution mismatch problem, respectively, we show that leveraging prior expert demonstrations for pre-training can reduce the training time to reach at least the same level of performance compared to plain RL by a factor of 5. We present a thorough evaluation of different combinations of expert demonstrations, different RL algorithms and reward functions, both in simulation and on a real robotic platform. Our results show that the final model outperforms both standalone approaches in the amount of successful navigation tasks. In addition, the RL reward function can be significantly simplified when using pre-training, e.g. by using a sparse reward only. The learned navigation policy is able to generalize to unseen and real-world environments. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep learning

Omnidirectional visual obstacle detection using embedded FPGA

学校读者我要写书评

暂无评论

Omnidirectional visual obstacle detection using embedded FPG...

IEEE/RSJ International Conference on Intelligent Robots and systems

作者： P. Gohl D. Honegger S. Omari M. Achtelik M. Pollefeys R. Siegwart ETH Zurich Autonomous Systems Lab Switzerland Computer Vision and Geometry Group ETH Zurich Switzerland Eidgenossische Technische Hochschule Zurich Zurich ZH CH

For autonomous navigation of Micro Aerial Vehicles (MAVs) in cluttered environments, it is essential to detect potential obstacles not only in the direction of flight but in their entire local environment. While there exist systems that do vision based obstacle detection, most of them are limited to a single perception direction. Extending these systems to a multi-directional sensing approach would exhaust the payload limit in terms of weight and computational power. We present a novel light-weight sensor setup comprising of four stereo heads and an inertial measurement unit (IMU) to perform FPGA-based dense reconstruction for obstacle detection in all directions. As the data-rate scales up with the number of cameras we use an FPGA to perform streaming based tasks in real-time and show a light-weight polar-coordinate map to allow a companion computer to fully process the data of all the cameras and perform obstacle detection in real-time. The system is able to process up to 80 frames per second (fps) freely distributed on the four stereo heads while maintaining a low power budget. The perception system including FPGA, image sensors and stereo mounts is 235 g in weight.

关键词： Robot sensing systems Cameras Collision avoidance Three-dimensional displays Field programmable gate arrays Head

Renovating Names in Open-Vocabulary Segmentation Benchmarks 38

学校读者我要写书评

暂无评论

Renovating Names in Open-Vocabulary Segmentation Benchmarks

38th Conference on Neural Information Processing systems, NeurIPS 2024

作者： Huang, Haiwen Peng, Songyou Zhang, Dan Geiger, Andreas Bosch IoC Lab University of Tübingen Germany Tübingen AI Center Germany Autonomous Vision Group University of Tübingen Germany ETH Zurich Switzerland MPI for Intelligent Systems Tübingen Germany Bosch Center for Artificial Intelligence Germany

Names are essential to both human cognition and vision-language models. Open-vocabulary models utilize class names as text prompts to generalize to categories unseen during training. However, the precision of these names is often overlooked in existing datasets. In this paper, we address this underexplored problem by presenting a framework for "renovating" names in open-vocabulary segmentation benchmarks (RENOVATE). Our framework features a renaming model that enhances the quality of names for each visual segment. Through experiments, we demonstrate that our renovated names help train stronger open-vocabulary models with up to 15% relative improvement and significantly enhance training efficiency with improved data quality. We also show that our renovated names improve evaluation by better measuring misclassification and enabling fine-grained model analysis. We provide our code and relabelings for several popular segmentation datasets to the research community on our project page: https://***/renovate/. © 2024 Neural information processing systems foundation. All rights reserved.

关键词：