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BO-ICP: Initialization of Iterative Closest Point Based on Bayesian Optimization

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BO-ICP: Initialization of Iterative Closest Point Based on B...

IEEE International Conference on robotics and Automation (ICRA)

作者： Harel Biggie Andrew Beathard Christoffer Heckman Autonomous Robotics and Perception Group University of Colorado Boulder Boulder Colorado USA

Typical algorithms for point cloud registration such as Iterative Closest Point (ICP) require a favorable initial transform estimate between two point clouds in order to perform a successful registration. State-of-the-art methods for choosing this starting condition rely on stochastic sampling or global optimization techniques such as branch and bound. In this work, we present a new method based on Bayesian optimization for finding the critical initial ICP transform. We provide three different configurations for our method which highlights the versatility of the algorithm to both find rapid results and refine them in situations where more runtime is available such as offline map building. Experiments are run on popular data sets and we show that our approach outperforms state-of-the-art methods when given similar computation time. Furthermore, it is compatible with other improvements to ICP, as it focuses solely on the selection of an initial transform, a starting point for all ICP-based methods.

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BO-ICP: Initialization of Iterative Closest Point Based on Bayesian Optimization

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arXiv 2023年

作者： Biggie, Harel Beathard, Andrew Heckman, Christoffer Autonomous Robotics and Perception Group The University of Colorado Boulder BoulderCO United States

关键词： Iterative methods

A New Wave in robotics: Survey on Recent mmWave Radar Applications in robotics

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arXiv 2023年

作者： Harlow, Kyle Jang, Hyesu Barfoot, Timothy D. Kim, Ayoung Heckman, Christoffer Autonomous Robotics and Perception Group the University of Colorado Boulder BoulderCO United States Robust Perception and Mobile Robotics Lab Seoul National University Seoul Korea Republic of Autonomous Space Robotics Laboratory the University of Toronto Toronto Canada

We survey the current state of millimeter-wave (mmWave) radar applications in robotics with a focus on unique capabilities, and discuss future opportunities based on the state of the art. Frequency Modulated Continuous Wave (FMCW) mmWave radars operating in the 76–81GHz range are an appealing alternative to lidars, cameras and other sensors operating in the near visual spectrum. Radar has been made more widely available in new packaging classes, more convenient for robotics and its longer wavelengths have the ability to bypass visual clutter such as fog, dust, and smoke. We begin by covering radar principles as they relate to robotics. We then review the relevant new research across a broad spectrum of robotics applications beginning with motion estimation, localization, and mapping. We then cover object detection and classification, and then close with an analysis of current datasets and calibration techniques that provide entry points into radar research. Copyright © 2023, The Authors. All rights reserved.

关键词： Motion estimation

Detecting Olives with Synthetic or Real Data? Olive the Above

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Detecting Olives with Synthetic or Real Data? Olive the Abov...

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

作者： Yianni Karabatis Xiaomin Lin Nitin J. Sanket Michail G. Lagoudakis Yiannis Aloimonos Perception and Robotics Group University of Maryland Institute for Advanced Computer Studies University of Maryland College Park MD USA Perception and Autonomous Robotics Group Robotics Engineering Worcester Polytechnic Institute MA USA School of ECE Technical University of Crete Greece

Modern robotics has enabled the advancement in yield estimation for precision agriculture. However, when applied to the olive industry, the high variation of olive colors and their similarity to the background leaf canopy presents a challenge. Labeling several thousands of very dense olive grove images for segmentation is a labor-intensive task. This paper presents a novel approach to detecting olives without the need to manually label data. In this work, we present the world's first olive detection dataset comprised of synthetic and real olive tree images. This is accomplished by generating an auto-labeled photorealistic 3D model of an olive tree. Its geometry is then simplified for lightweight rendering purposes. In addition, experiments are conducted with a mix of synthetically generated and real images, yielding an improvement of up to 66% compared to when only using a small sample of real data. When access to real, human-labeled data is limited, a combination of mostly synthetic data and a small amount of real data can enhance olive detection.

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Detecting Olives with Synthetic or Real Data? Olive the Above

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arXiv 2023年

作者： Karabatis, Yianni Lin, Xiaomin Sanket, Nitin J. Lagoudakis, Michail G. Aloimonos, Yiannis Perception and Robotics Group University of Maryland Institute for Advanced Computer Studies University of Maryland College ParkMD20742 United States Perception and Autonomous Robotics Group Robotics Engineering Worcester Polytechnic Institute MA01609 United States School of ECE Technical University of Crete Greece

关键词： Image enhancement

deepNIR: Datasets for generating synthetic NIR images and improved fruit detection system using deep learning techniques

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arXiv 2022年

作者： Sa, Inkyu Lim, JongYoon Ahn, Ho Seok MacDonald, Bruce CSIRO Data61 Robotics and Autonomous Systems Group Robot Perception Team Switzerland CARES Department of Electrical Computer and Software Engineering University of Auckland New Zealand

This paper presents datasets utilised for synthetic near-infrared (NIR) image generation and bounding-box level fruit detection systems. A high-quality dataset is one of the essential building blocks that can lead to success in model generalisation and the deployment of data-driven deep neural networks. In particular, synthetic data generation tasks often require more training samples than other supervised approaches. Therefore, in this paper, we share the NIR+RGB datasets that are re-processed from two public datasets (i.e., nirscene and SEN12MS), expanded our previous study, deepFruits, and our novel NIR+RGB sweet pepper(capsicum) dataset. We oversampled from the original nirscene dataset at 10, 100, 200, and 400 ratios that yielded a total of 127 k pairs of images. From the SEN12MS satellite multispectral dataset, we selected Summer (45 k) and All seasons (180 k) subsets and applied a simple yet important conversion;digital number (DN) to pixel value conversion followed by image standardisation. Our sweet pepper dataset consists of 1615 pairs of NIR+RGB images that were collected from commercial farms. We quantitatively and qualitatively demonstrate that these NIR+RGB datasets are sufficient to be used for synthetic NIR image generation. We achieved Frechet Inception Distance (FID) of 11.36, 26.53, and 40.15 for nirscene1, SEN12MS, and sweet pepper datasets respectively. In addition, we release manual annotations of 11 fruit bounding boxes that can be exported as various formats using cloud service. Four newly added fruits [blueberry, cherry, kiwi, and wheat] compound 11 novel bounding box datasets on top of our previous work presented in the deepFruits project [apple, avocado, capsicum, mango, orange, rockmelon, strawberry]. The total number of bounding box instances of the dataset is 162 k and it is ready to use from cloud service. For the evaluation of the dataset, Yolov5 single stage detector is exploited and reported impressive mean-average-precision, mAP[0.

关键词： Generative adversarial networks

A Benchmark for Visual-Inertial Odometry Systems Employing Onboard Illumination

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A Benchmark for Visual-Inertial Odometry Systems Employing O...

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Mike Kasper Steve McGuire Christoffer Heckman Autonomous Robotics and Perception Group (APRG) University of Colorado. Boulder Colorado USA

We present a dataset for evaluating the performance of visual-inertial odometry (VIO) systems employing an onboard light source. The dataset consists of 39 sequences, recorded in mines, tunnels, and other dark environments, totaling more than 160 minutes of stereo camera video and IMU data. In each sequence, the scene is illuminated by an onboard light of approximately 1300, 4500, or 9000 lumens. We accommodate both direct and indirect visual odometry methods by providing the geometric and photometric camera calibrations (i.e. response, attenuation, and exposure times). In contrast with existing datasets, we also calibrate the light source itself and publish data for inferring more complex light models. Ground-truth position data are available for a subset of sequences, as captured by a Leica total station. All remaining sequences start and end at the same position, permitting the use of total accumulated drift as a metric for evaluation. Using our proposed benchmark, we analyze the performance of several start-of-the-art VO and VIO frame-works. The full dataset, including sensor data, calibration sequences, and evaluation scripts, is publicly available online at http://***/research/oivio.

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Online System Identification and Calibration of Dynamic Models for autonomous Ground Vehicles

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Online System Identification and Calibration of Dynamic Mode...

IEEE International Conference on robotics and Automation (ICRA)

作者： Sina Aghli Christoffer Heckman Both authors are with the University of Colorado BBoulder Autonomous Robotics and Perception Group

This paper is concerned with system identification and the calibration of parameters of dynamic models used in different robotic platforms. A constant time algorithm has been developed in order to automatically calibrate the parameters of a high-fidelity dynamical model for a robotic platform. The presented method is capable of choosing informative motion segments in order to calibrate model parameters in constant time while also calculating a confidence level on each estimated parameter. Simulations and experiments with a ⅛ th scale four wheel drive vehicle are performed to calibrate two of the parameters of test vehicle which demonstrate the accuracy and efficiency of the approach.

关键词： Calibration Vehicle dynamics Motion segmentation Dynamics Wheels Friction

Online Probabilistic Change Detection in Feature-Based Maps

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Online Probabilistic Change Detection in Feature-Based Maps

IEEE International Conference on robotics and Automation

作者： Fernando Nobre Christoffer Heckman Paul Ozog Ryan W. Wolcott Jeffrey M. Walls University of Colorado Boulder Autonomous Robotics and Perception Group Toyota Research Institute

Sparse feature-based maps provide a compact representation of the environment that admit efficient algorithms, for example simultaneous localization and mapping. These representations typically assume a static world and therefore contain static map features. However, since the world contains dynamic elements, determining when map features no longer correspond to the environment is essential for long-term utility. This work develops a feature-based model of the environment which evolves over time through feature persistence. Moreover, we augment the state-of-the-art sparse mapping model with a correlative structure that captures spatio-temporal properties, e.g. that nearby features frequently have similar persistence. We show that such relationships, typically addressed through an ad hoc formalism focusing only on feature repeatability, are crucial to evaluate through a probabilistically principled approach. The joint posterior over feature persistence can be computed efficiently and used to improve online data association decisions for localization. The proposed algorithms are validated in numerical simulation and using publicly available data sets.

关键词： Feature extraction Simultaneous localization and mapping Robustness Heuristic algorithms Probabilistic logic Visualization