Occlusion Handling for Industrial Robots

Zhu, Ling; Menon, Meghna; Santillo, Mario; Linkowski, Gregory

doi:10.1109/iros45743.2020.9341329

Cited by 3 publications

(2 citation statements)

References 20 publications

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“…However, methods utilizing vision or lasers come with the drawback of requiring large computational resources [27,28]. Additionally, vision and laser sensing faces challenges when temporary occlusions occur, such as when an obstacle momentarily obscures the person, making it difficult to discern their posture [29,30].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Human Movement Recognition Using Ultra-Wideband Sensors

Noh,

Ahn,

Lee

2024

Electronics

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This study introduces a methodology for the real-time detection of human movement based on two legs using ultra-wideband (UWB) sensors. Movements were primarily categorized into four states: stopped, walking, lingering, and the transition between sitting and standing. To classify these movements, UWB sensors were used to measure the distance between the designated point and a specific point on the two legs in the human body. By analyzing the measured distance values, a movement state classification model was constructed. In comparison to conventional vision/laser/LiDAR-based research, this approach requires fewer computational resources and provides distinguished real-time human movement detection within a CPU environment. Consequently, this research presents a novel strategy to effectively recognize human movements during human–robot interactions. The proposed model effectively discerned four distinct movement states with classification accuracy of around 95%, demonstrating the novel strategy’s efficacy.

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Section: Introductionmentioning

confidence: 99%

Real-Time Human Movement Recognition Using Ultra-Wideband Sensors

Noh,

Ahn,

Lee

2024

Electronics

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“…Multi-camera systems offer the advantage of monitoring larger areas and making several computer vision algorithms more robust against occlusion problems. These challenges frequently occur in complex real-world scenarios such as people tracking applications [5] or robotic workcells [6].…”

Section: Introductionmentioning

confidence: 99%

METRIC—Multi-Eye to Robot Indoor Calibration Dataset

2023

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Multi-camera systems are an effective solution for perceiving large areas or complex scenarios with many occlusions. In such a setup, an accurate camera network calibration is crucial in order to localize scene elements with respect to a single reference frame shared by all the viewpoints of the network. This is particularly important in applications such as object detection and people tracking. Multi-camera calibration is a critical requirement also in several robotics scenarios, particularly those involving a robotic workcell equipped with a manipulator surrounded by multiple sensors. Within this scenario, the robot-world hand-eye calibration is an additional crucial element for determining the exact position of each camera with respect to the robot, in order to provide information about the surrounding workspace directly to the manipulator. Despite the importance of the calibration process in the two scenarios outlined above, namely (i) a camera network, and (ii) a camera network with a robot, there is a lack of standard datasets available in the literature to evaluate and compare calibration methods. Moreover they are usually treated separately and tested on dedicated setups. In this paper, we propose a general standard dataset acquired in a robotic workcell where calibration methods can be evaluated in two use cases: camera network calibration and robot-world hand-eye calibration. The Multi-Eye To Robot Indoor Calibration (METRIC) dataset consists of over 10,000 synthetic and real images of ChAruCo and checkerboard patterns, each one rigidly attached to the robot end-effector, which was moved in front of four cameras surrounding the manipulator from different viewpoints during the image acquisition. The real images in the dataset includes several multi-view image sets captured by three different types of sensor networks: Microsoft Kinect V2, Intel RealSense Depth D455 and Intel RealSense Lidar L515, to evaluate their advantages and disadvantages for calibration. Furthermore, in order to accurately analyze the effect of camera-robot distance on calibration, we acquired a comprehensive synthetic dataset, with related ground truth, with three different camera network setups corresponding to three levels of calibration difficulty depending on the cell size. An additional contribution of this work is to provide a comprehensive evaluation of state-of-the-art calibration methods using our dataset, highlighting their strengths and weaknesses, in order to outline two benchmarks for the two aforementioned use cases.

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BSL: Navigation Method Considering Blind Spots Based on ROS Navigation Stack and Blind Spots Layer for Mobile Robot

Kobayashi,

Motoi

2024

IEEE Trans. on Ind. Applicat.

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Occlusion Handling for Industrial Robots

Cited by 3 publications

References 20 publications

Real-Time Human Movement Recognition Using Ultra-Wideband Sensors

Real-Time Human Movement Recognition Using Ultra-Wideband Sensors

METRIC—Multi-Eye to Robot Indoor Calibration Dataset

BSL: Navigation Method Considering Blind Spots Based on ROS Navigation Stack and Blind Spots Layer for Mobile Robot

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