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PointPillars Backbone Type Selection For Fast and Accurate LiDAR Object Detection

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arXiv 2022年

作者： Lis, Konrad Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Al. Mickiewicza 30 Krakow30-059 Poland

3D object detection from LiDAR sensor data is an important topic in the context of autonomous cars and drones. In this paper, we present the results of experiments on the impact of backbone selection of a deep convolutional neural network on detection accuracy and computation speed. We chose the PointPillars network, which is characterised by a simple architecture, high speed, and modularity that allows for easy expansion. During the experiments, we paid particular attention to the change in detection efficiency (measured by the mAP metric) and the total number of multiply-addition operations needed to process one point cloud. We tested 10 different convolutional neural network architectures that are widely used in image-based detection problems. For a backbone like MobilenetV1, we obtained an almost 4x speedup at the cost of a 1.13% decrease in mAP. On the other hand, for CSPDarknet we got an acceleration of more than 1.5x at an increase in mAP of 0.33%. We have thus demonstrated that it is possible to significantly speed up a 3D object detector in LiDAR point clouds with a small decrease in detection efficiency. This result can be used when PointPillars or similar algorithms are implemented in embedded systems, including SoC FPGAs. The code is available at https://***/vision-agh/pointpillars backbone. © 2022, CC BY.

关键词： Object detection

Achachay App: a community-driven innovation for flood data collection in urban and rural areas

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Achachay App: a community-driven innovation for flood data c...

International Conference on Technological Innovation and AI Research (ICTIAIR 2025)

作者： Ariana Deyaneira Jiménez-Narváez Jonathan Javier Loor-Duque Diego Josue Andrade-Pelaez Manuel Eugenio Morocho-Cayamcela Raisa Torres-Ramírez Deep Learning for Autonomous Driving Robotics and Computer Vision Research Group (DeepARC) School of Mathematical and Computational Sciences Yachay Tech University San Miguel de Urcuquí Ecuador Applied Geology and Research Group School of Geological Sciences Energy and Environment Yachay Tech University San Miguel de Urcuquí Ecuador

ISBN: (数字)9781837243235

Floods are among the most frequent and devastating natural disasters, significantly impacting urban and rural communities worldwide. Achachay App is a mobile application designed to enhance flood data collection and analysis through direct community engagement. The app enables users to report real-time flood events by sharing detailed descriptions, geographic coordinates, and multimedia evidence such as photos or videos. Researchers use these community-sourced data to analyze and reconstruct watercourse behaviors that lead to flooding. Ecuador was selected for implementation as a pilot initiative, where the app successfully mapped flood-prone areas and secondary flows. These features aim to deepen understanding of the interplay between geological and climatic phenomena, supporting disaster preparedness and prevention. By bridging technology and community collaboration, the Achachay Appoffers valuable insights for policymakers to design sustainable public policies that mitigate risks and promote social and economic development in Ecuador.

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P1AC: Revisiting Absolute Pose From a Single Affine Correspondence

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P1AC: Revisiting Absolute Pose From a Single Affine Correspo...

International Conference on computer vision (ICCV)

作者： Jonathan Ventura Zuzana Kukelova Torsten Sattler Dániel Baráth Department of Computer Science & Software Engineering Cal Poly San Luis Obispo Visual Recognition Group Faculty of Electrical Engineering Czech Technical University in Prague Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Computer Vision and Geometry Group ETH Zürich

Affine correspondences have traditionally been used to improve feature matching over wide baselines. While recent work has successfully used affine correspondences to solve various relative camera pose estimation problems, less attention has been given to their use in absolute pose estimation. We introduce the first general solution to the problem of estimating the pose of a calibrated camera given a single observation of an oriented point and an affine correspondence. The advantage of our approach (P1AC) is that it requires only a single correspondence, in comparison to the traditional point-based approach (P3P), significantly reducing the combinatorics in robust estimation. P1AC provides a general solution that removes restrictive assumptions made in prior work and is applicable to large-scale image-based localization. We propose a minimal solution to the P1AC problem and evaluate our novel solver on synthetic data, showing its numerical stability and performance under various types of noise. On standard image-based localization benchmarks we show that P1AC achieves more accurate results than the widely used P3P algorithm. Code for our method is available at https://***/jonathanventura/P1AC/.

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Convolutional Cross-View Pose Estimation

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arXiv 2023年

作者： Xia, Zimin Booij, Olaf Kooij, Julian F.P. The Intelligent Vehicles Group Department of Cognitive Robotics Delft University of Technology Leeghwaterstraat Delft2628 CN Netherlands The Computer Vision Lab Delft University of Technology Mekelweg 4 Delft2628 CD Netherlands

We propose a novel end-to-end method for cross-view pose estimation. Given a ground-level query image and an aerial image that covers the query’s local neighborhood, the 3 Degrees-of-Freedom camera pose of the query is estimated by matching its image descriptor to descriptors of local regions within the aerial image. The orientation-aware descriptors are obtained by using a translationally equivariant convolutional ground image encoder and contrastive learning. The Localization Decoder produces a dense probability distribution in a coarse-to-fine manner with a novel Localization Matching Upsampling module. A smaller Orientation Decoder produces a vector field to condition the orientation estimate on the localization. Our method is validated on the VIGOR and KITTI datasets, where it surpasses the state-of-the-art baseline by 72% and 36% in median localization error for comparable orientation estimation accuracy. The predicted probability distribution can represent localization ambiguity, and enables rejecting possible erroneous predictions. Without re-training, the model can infer on ground images with different field of views and utilize orientation priors if available. On the Oxford RobotCar dataset, our method can reliably estimate the ego-vehicle’s pose over time, achieving a median localization error under 1 meter and a median orientation error of around 1 degree at 14 FPS. Copyright © 2023, The Authors. All rights reserved.

关键词： Cameras

DA-LMR: A Robust Lane Marking Representation for Data Association

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DA-LMR: A Robust Lane Marking Representation for Data Associ...

IEEE International Conference on robotics and Automation (ICRA)

作者： Miguel Á ngel Muñ oz-Bañ ó n Jan-Hendrik Pauls Haohao Hu Christoph Stiller Group of Automation Robotics and Computer Vision (AUROVA) University of Alicante Alicante Spain Institute of Measurement and Control Systems Karlsruhe Institute of Technology Karlsruhe Germany

While complete localization approaches are widely studied in the literature, their data association and data representation subprocesses usually go unnoticed. However, both are a key part of the final pose estimation. In this work, we present DA-LMR (Delta-Angle Lane Marking Representation), a robust data representation in the context of localization approaches. We propose a representation of lane markings that encodes how a curve changes in each point and includes this information in an additional dimension, thus providing a more detailed geometric structure description of the data. We also propose DC-SAC (Distance-Compatible Sample Consensus), a data association method. This is a heuristic version of RANSAC that dramatically reduces the hypothesis space by distance compatibility restrictions. We compare the presented methods with some state-of-the-art data representation and data association approaches in different noisy scenarios. The DA-LMR and DC-SAC produce the most promising combination among those compared, reaching 98.1 % in precision and 99.7% in recall for noisy data with 0.5 m of standard deviation.

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Optimisation of a Siamese Neural Network for Real-Time Energy Efficient Object Tracking 1

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International Conference on computer vision and Graphics, ICCVG 2020

作者： Przewlocka, Dominika Wasala, Mateusz Szolc, Hubert Blachut, Krzysztof Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Krakow Poland

ISBN: (数字)9783030590062

ISBN: (纸本)9783030590055

In this paper the research on optimisation of visual object tracking using a Siamese neural network for embedded vision systems is presented. It was assumed that the solution shall operate in real-time, preferably for a high resolution video stream, with the lowest possible energy consumption. To meet these requirements, techniques such as the reduction of computational precision and pruning were considered. Brevitas, a tool dedicated for optimisation and quantisation of neural networks for FPGA implementation, was used. A number of training scenarios were tested with varying levels of optimisations – from integer uniform quantisation with 16 bits to ternary and binary networks. Next, the influence of these optimisations on the tracking performance was evaluated. It was possible to reduce the size of the convolutional filters up to 10 times in relation to the original network. The obtained results indicate that using quantisation can significantly reduce the memory and computational complexity of the proposed network while still enabling precise tracking, thus allow to use it in embedded vision systems. Moreover, quantisation of weights positively affects the network training by decreasing overfitting. © 2020, Springer Nature Switzerland AG.

关键词： Field programmable gate arrays (FPGA)

A vision Based Hardware-Software Real-Time Control System for the Autonomous Landing of an UAV 1

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International Conference on computer vision and Graphics, ICCVG 2020

作者： Blachut, Krzysztof Szolc, Hubert Wasala, Mateusz Kryjak, Tomasz Gorgon, Marek Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Krakow Poland

ISBN: (数字)9783030590062

ISBN: (纸本)9783030590055

In this paper we present a vision based hardware-software control system enabling the autonomous landing of a multirotor unmanned aerial vehicle (UAV). It allows for the detection of a marked landing pad in real-time for a 1280 720 @ 60 fps video stream. In addition, a LiDAR sensor is used to measure the altitude above ground. A heterogeneous Zynq SoC device is used as the computing platform. The solution was tested on a number of sequences and the landing pad was detected with 96% accuracy. This research shows that a reprogrammable heterogeneous computing system is a good solution for UAVs because it enables real-time data stream processing with relatively low energy consumption. © 2020, Springer Nature Switzerland AG.

关键词： System-on-chip

Learning Deep Sensorimotor Policies for vision-based Autonomous Drone Racing

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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

Traffic Sign Detection With Event Cameras and DCNN

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arXiv 2022年

作者： Wzorek, Piotr Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Kraków Poland Department of Digital Systems Silesian University of Technology Gliwice Poland

In recent years, event cameras (DVS – Dynamic vision Sensors) have been used in vision systems as an alternative or supplement to traditional cameras. They are characterised by high dynamic range, high temporal resolution, low latency, and reliable performance in limited lighting conditions – parameters that are particularly important in the context of advanced driver assistance systems (ADAS) and self-driving cars. In this work, we test whether these rather novel sensors can be applied to the popular task of traffic sign detection. To this end, we analyse different representations of the event data: event frame, event frequency, and the exponentially decaying time surface, and apply video frame reconstruction using a deep neural network called FireNet. We use the deep convolutional neural network YOLOv4 as a detector. For particular representations, we obtain a detection accuracy in the range of 86.9-88.9% mAP@0.5. The use of a fusion of the considered representations allows us to obtain a detector with higher accuracy of 89.9% mAP@0.5. In comparison, the detector for the frames reconstructed with FireNet is characterised by an accuracy of 72.67% mAP@0.5. The results obtained illustrate the potential of event cameras in automotive applications, either as standalone sensors or in close cooperation with typical frame-based cameras. © 2022, CC BY.

关键词： Cameras