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On Onboard LiDAR-Based Flying Object Detection

IEEE TRANSACTIONS ON ROBOTICS 2025年 41卷 593-611页

作者： Vrba, Matous Walter, Viktor Pritzl, Vaclav Pliska, Michal Baca, Tomas Spurny, Vojtech Hert, Daniel Saska, Martin Czech Tech Univ Fac Elect Engn Prague 16627 Czech Republic

A new robust and accurate approach for the detection and localization of flying objects with the purpose of highly dynamic aerial interception and agile multirobot interaction is presented in this article. The approach is proposed for use on board of autonomous aerial vehicles equipped with a 3-D LiDAR sensor. It relies on a novel 3-D occupancy voxel mapping method for the target detection that provides high localization accuracy and robustness with respect to varying environments and appearance changes of the target. In combination with a proposed cluster-based multitarget tracker, sporadic false positives are suppressed, state estimation of the target is provided, and the detection latency is negligible. This makes the system suitable for tasks of agile multirobot interaction, such as autonomous aerial interception or formation control where fast, precise, and robust relative localization of other robots is crucial. We evaluate the viability and performance of the system in simulated and real-world experiments which demonstrate that at a range of 20 m, our system is capable of reliably detecting a microscale UAV with an almost 100% recall, 0.2 m accuracy, and 20 ms delay.

关键词： Autonomous aerial vehicles Robot sensing systems Location awareness Robots Point cloud compression Accuracy Detectors Laser radar Three-dimensional displays Target tracking Aerial systems autonomous aerial interception multirobot systems object detection perception and autonomy segmentation and categorization

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Cherry CO Dataset: A Dataset for Cherry Detection, segmentation and Maturity Recognition

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IEEE ROBOTICS AND AUTOMATION LETTERS 2024年第6期9卷 5552-5558页

作者： Cossio-Montefinale, Luis Ruiz-del-Solar, Javier Verschae, Rodrigo Univ Chile Dept Elect Engn Santiago 8330015 Chile Univ Chile Adv Min Technol Ctr AMTC Santiago 8330015 Chile Univ OHiggins Inst Engn Sci Rancagua 2820000 Chile

In recent years, there has been an increasing interest in using robotics and autonomous systems for fruit farming. These systems use detection and segmentation algorithms to provide computers with a way to interpret and interact with plants, thus allowing them to automatize assessment and harvesting tasks. This type of algorithm requires a dataset to train a machine learning algorithm, but most types of fruits lack a publicly available dataset. We present Cherry CO, a novel, high-resolution dataset for cherry detection and segmentation. This dataset contains 3,006 labeled images that were acquired in a cherry plantation. The images were taken in various conditions to account for most challenges in this environment. Each cherry was manually labeled to account for location, shape and classes, taking into account aspects such as ripeness, health state and location. This letter presents a detailed description of data acquisition, annotation specifications and statistical analysis of the dataset. In addition, we trained and evaluated several Deep Neural Networks to provide a benchmark for cherry detection, achieving high performance with most networks, most notably YOLOv7. The dataset and evaluation software are provided for research purposes.

关键词： Agricultural automation data sets for robot learning deep learning for visual perception object detection segmentation and categorization

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VRVP: Valuable Region and Valuable Point Anchor-Free 3D Object Detection

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IEEE ROBOTICS AND AUTOMATION LETTERS 2024年第1期9卷 33-40页

作者： Deng, Pengzhen Zhou, Li Chen, Jie Chinese Acad Sci Inst Microelect Beijing 100029 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

3D point cloud object detection is of great significance in autonomous driving, robotics, and related fields. However, the current algorithms fail to fully consider that positive sample points of a point cloud object exclusively reside on its surface, resulting in decreased accuracy. High-quality classification points are located centrally, while high-quality regression points are predominantly situated at the boundary. We propose an anchor-free object detection algorithm called Valuable Region and Valuable Point (VRVP), aiming to address the inconsistency between the classification and regression tasks. We achieve high-quality object size regression through a classification-based approach and implicitly correlate the regression and classification branches. Furthermore, a valuable region extraction module is introduced to select valuable points and regions and fill in the missing features of the object's center area. We validate the effectiveness of our algorithm on both the KITTI dataset and Waymo dataset by comparing it with state-of-the-art algorithms. The results demonstrate that the proposed method exhibits strong competitiveness by significantly improving detection accuracy for small objects such as pedestrians and cyclists, while maintaining high accuracy in detecting cars.

关键词： Feature extraction Three-dimensional displays Point cloud compression Object detection Task analysis Convolution Prediction algorithms Computer vision for automation deep learning for visual perception object detection segmentation and categorization

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Follow Anything: Open-Set Detection, Tracking, and Following in Real-Time

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IEEE ROBOTICS AND AUTOMATION LETTERS 2024年第4期9卷 3283-3290页

作者： Maalouf, Alaa Jadhav, Ninad Jatavallabhula, Krishna Murthy Chahine, Makram Vogt, Daniel M. Wood, Robert J. Torralba, Antonio Rus, Daniela MIT Comp Sci & Artificial Intelligence Lab Cambridge MA 02139 USA CETI New York NY 10003 USA Harvard Univ John A Paulson Sch Engn & Appl Sci Boston MA 02138 USA

Tracking and following objects of interest is critical to several robotics use cases, ranging from industrial automation to logistics and warehousing, to healthcare and security. In this letter, we present a robotic system to detect, track, and follow any object in real-time. Our approach, dubbed follow anything (FAn), is an open-vocabulary and multimodal model - it is not restricted to concepts seen at training time and can be applied to novel classes at inference time using text, images, or click queries. Leveraging rich visual descriptors from large-scale pre-trained models (foundation models), FAn can detect and segment objects by matching multimodal queries (text, images, clicks) against an input image sequence. These detected and segmented objects are tracked across image frames, all while accounting for occlusion and object re-emergence. We demonstrate FAn on a real-world robotic system (a micro aerial vehicle), and report its ability to seamlessly follow the objects of interest in a real-time control loop. FAn can be deployed on a laptop with a lightweight (6-8 GB) graphics card, achieving a throughput of 6-20 frames per second. To enable rapid adoption, deployment, and extensibility, we open-source our code on our project webpage. We also encourage the reader to watch our 5-minute explainer video.

关键词： AI-enabled robotics object detection segmentation and categorization semantic scene understanding

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DUFOMap: Efficient Dynamic Awareness Mapping

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IEEE ROBOTICS AND AUTOMATION LETTERS 2024年第6期9卷 5038-5045页

作者： Duberg, Daniel Zhang, Qingwen Jia, Mingkai Jensfelt, Patric KTH Royal Inst Technol Div Robot Percept & Learning RPL Stockholm 114 28 Sweden Hong Kong Univ Sci & Technol Robot Inst Hong Kong 999077 Peoples R China

The dynamic nature of the real world is one of the main challenges in robotics. The first step in dealing with it is to detect which parts of the world are dynamic. A typical benchmark task is to create a map that contains only the static part of the world to support, for example, localization and planning. Current solutions are often applied in post-processing, where parameter tuning allows the user to adjust the setting for a specific dataset. In this letter, we propose DUFOMap, a novel dynamic awareness mapping framework designed for efficient online processing. Despite having the same parameter settings for all scenarios, it performs better or is on par with state-of-the-art methods. Ray casting is utilized to identify and classify fully observed empty regions. Since these regions have been observed empty, it follows that anything inside them at another time must be dynamic. Evaluation is carried out in various scenarios, including outdoor environments in KITTI and Argoverse 2, open areas on the KTH campus, and with different sensor types. DUFOMap outperforms the state of the art in terms of accuracy and computational efficiency.

关键词： Mapping object detection segmentation and categorization robotics and automation in construction

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BeautyMap: Binary-Encoded Adaptable Ground Matrix for Dynamic Points Removal in Global Maps

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IEEE ROBOTICS AND AUTOMATION LETTERS 2024年第7期9卷 6256-6263页

作者： Jia, Mingkai Zhang, Qingwen Yang, Bowen Wu, Jin Liu, Ming Jensfelt, Patric Hong Kong Univ Sci & Technol Robot Inst Hong Kong Peoples R China KTH Royal Inst Technol Div Robot Percept & Learning RPL S-11428 Stockholm Sweden Hong Kong Univ Sci & Technol Guangzhou Guangzhou 511400 Peoples R China

Global point clouds that correctly represent the static environment features can facilitate accurate localization and robust path planning. However, dynamic objects introduce undesired 'ghost' tracks that are mixed up with the static environment. Existing dynamic removal methods normally fail to balance the performance in computational efficiency and accuracy. In response, we present 'BeautyMap' to efficiently remove the dynamic points while retaining static features for high-fidelity global maps. Our approach utilizes a binary-encoded matrix to efficiently extract the environment features. With a bit-wise comparison between matrices of each frame and the corresponding map region, we can extract potential dynamic regions. Then we use coarse to fine hierarchical segmentation of the z -axis to handle terrain variations. The final static restoration module accounts for the range-visibility of each single scan and protects static points out of sight. Comparative experiments underscore BeautyMap's superior performance in both accuracy and efficiency against other dynamic points removal methods.

关键词： Object detection segmentation and categorization mapping autonomous agents

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ParisLuco3D: A High-Quality Target Dataset for Domain Generalization of LiDAR Perception

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IEEE ROBOTICS AND AUTOMATION LETTERS 2024年第6期9卷 5496-5503页

作者： Sanchez, Jules Soum-Fontez, Louis Deschaud, Jean-Emmanuel Goulette, Francois PSL Univ Ctr Robot Mines Paris PSL F-75006 Paris France Inst Polytech Paris ENSTA Paris U2IS F-91120 Palaiseau France

LiDAR is an essential sensor for autonomous driving by collecting precise geometric information regarding a scene. As the performance of various LiDAR perception tasks has improved, generalizations to new environments and sensors has emerged to test these optimized models in real-world conditions. Unfortunately, the various annotation strategies of data providers complicate the computation of cross-domain performances. This paper provides a novel dataset, ParisLuco3D, specifically designed for cross-domain evaluation to make it easier to evaluate the performance utilizing various source datasets. Alongside the dataset, online benchmarks for LiDAR semantic segmentation, LiDAR object detection, and LiDAR tracking are provided to ensure a fair comparison across methods.

关键词： Laser radar Annotations Semantic segmentation Task analysis Object detection Point cloud compression Robot sensing systems Data sets for robotic vision intelligent transportation systems object detection segmentation and categorization

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3D Detection and Tracking of a Moving Object by an Autonomous Underwater Vehicle With a Multibeam Imaging Sonar: Toward Continuous Observation of Marine Life

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IEEE ROBOTICS AND AUTOMATION LETTERS 2024年第4期9卷 3037-3044页

作者： Chun, Sehwa Kawamura, Chihaya Ohkuma, Kenji Maki, Toshihiro Univ Tokyo Inst Ind Sci Tokyo 1538505 Japan

Biologging has been the prevalent method for studying the ecology and behavior of aquatic life. However, it has problems of operational inconvenience and stress on organisms. Recently, research towards remote tracking using Autonomous Underwater Vehicles (AUVs) has been gaining attention, particularly those equipped with multibeam imaging sonar (MBS) less affected by underwater light and turbidity. However, existing studies have focused mainly on 2D detection and tracking due to the inherent limitations of MBS. In this letter, we propose a novel method for 3D detection and tracking of a moving target for marine life surveys, utilizing the tilt control of MBS on AUVs. YOLOv3 is employed to identify the target's position in sonar imagery. Incorporation of the particle filter algorithm enhances position an estimation accuracy and enables a estimation of the target's state, velocity and orientation as well. 3D tracking method is introduced based on the estimated states. This method has been validated through tank experiments with a hovering-type AUV and a life-sized sea turtle replica, successfully achieving 3D tracking of the target moving horizontally and vertically. The approach demonstrated a position estimation with an RMSE of 0.23 m and velocity and orientation within the error of 0.022 m/s and 16.59 degrees, respectively.

关键词： Marine robotics object detection segmentation and categorization AI-enabled robotics autonomous underwater vehicle target tracking

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Toward Robust Robot 3-D Perception in Urban Environments: The UT Campus Object Dataset

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IEEE TRANSACTIONS ON ROBOTICS 2024年 40卷 3322-3340页

作者： Zhang, Arthur Eranki, Chaitanya Zhang, Christina Park, Ji-Hwan Hong, Raymond Kalyani, Pranav Kalyanaraman, Lochana Gamare, Arsh Bagad, Arnav Esteva, Maria Biswas, Joydeep Univ Texas Austin Austin TX 78705 USA

We introduce the UT Campus Object Dataset (CODa), a mobile robot egocentric perception dataset collected on the University of Texas Austin Campus. Our dataset contains 8.5 h of multimodal sensor data from 3-D light detection and ranging (LiDAR), stereo RGB and rgb and depth (RGBD) cameras, and a 9-DoF inertial measurement unit (IMU). CODa contains 58 min of ground truth annotations containing 1.3 million 3-D bounding boxes with instance identifiers (ID) for 53 semantic classes, 5000 frames of 3-D semantic annotations for urban terrain, and pseudoground truth localization. We repeatedly traverse identical geographic regions for diverse indoor and outdoor areas, weather conditions, and times of the day. Using CODa, we empirically demonstrate that: 1) 3-D object detection performance improves in urban settings when trained using CODa compared with existing datasets, 2) sensor-specific fine-tuning increases 3-D object detection accuracy, and 3) pretraining on CODa improves cross-dataset 3-D object detection performance in urban settings compared with pretraining on AV datasets. We release benchmarks for 3-D object detection and 3-D semantic segmentation, with future plans for additional tasks. We publicly release CODa on the Texas Data Repository (Zhang et al., 2023), pretrained models, dataset development package, and interactive dataset viewer. We expect CODa to be a valuable dataset for egocentric perception and planning for navigation in urban environments.

关键词： Data sets for robotic ision object detection performance evaluation and benchmarking segmentation and categorization service robots

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Enhanced Optical Tracking of Weld Beads in Autonomous Inspection of Separator Vessels

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IEEE ROBOTICS AND AUTOMATION LETTERS 2024年第1期9卷 167-174页

作者： Terres, Vinicius De Vargas Simoes Teixeira, Marco Antonio Neves, Flavio, Jr. Ramos de Arruda, Lucia Valeria de Oliveira, Andre Schneider Univ Tecnol Fed Parana UTFPR BR-80230901 Curitiba Parana Brazil Pontificia Univ Catolica Parana PUCPR Rua Imaculada Conceicao BR-1155 Curitiba Parana Brazil

Inspection robots have been developed to support the maintenance of separator vessels. One challenge for such robots is to identify and navigate along the weld bead. This paper proposes a new solution to the weld bead recognition problem, including its tracking, which aims to automate weld bead inspection. A reliable weld bead detection method was developed through five steps that process the line profile sensor data. The weld bead was identified based on the estimation of its center. A non-linear controller paired with a module that compensates for uncertainties generated by gravity was designed to ensure the tracking of weld bead. Such a controller can reduce the impact of the variation in the robot's orientation on the weld bead tracking. An algorithm redirects the robot at the end of the weld bead. The experiments were conducted in a laboratory-scale setup, allowing movement along horizontal and diagonal surfaces. The results show that the robot tracked the weld line in all simulated conditions, with a maximum error of 2.86 mm.

关键词： Object detection segmentation and categorization autonomous agents autonomous inspection robot weld bead recognition weld bead tracking

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