Circular Object Detection in Polar Coordinates for 2D LIDAR Data

Zhou, Xianen; Wang, Yaonan; Qing, Zhu; Miao, Zhiqiang

doi:10.1007/978-981-10-3002-4_6

Cited by 9 publications

(10 citation statements)

References 13 publications

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“…While this connection between robots can be provided by Wi-Fi networks and GPS systems, the unreliability of these networks and the amount of communicated data that grows gradually with the number of robots in the team keeps the need for an alternative solution based on local sensing. Several studies have dealt with this subject using a variety of sensors and methods [20,21,22], but there are few studies performing kin detection using lidar [23,24,25]. For this reason, as a case study for our new robot, we proposed a new geometric kin detection method and tested it with two different scenarios.…”

Section: Discussionmentioning

confidence: 99%

“…In future works, further research and more systematic experiments will be performed to improve the kin detection algorithm. One example of such works is reimplementing the segment classification stage using machine learning algorithms as in [25].…”

Section: Discussionmentioning

confidence: 99%

“…Many collective behaviors tasks such as aggregation [27], pattern formation [28] and flocking [29] have been developed using simulators but regardless of the type of task required, detecting surrounding objects in the environment and distinguishing robots from these objects are essential for robot's operations. Several studies have proposed solutions to handle this issue which is often called "kin detection task" using different types of sensor like, infrared [20], ultrasound [21], vision [22], and Lidar [23,24,25] sensors. Lidar-based studies use a common approach which depends on using circular robots or any circular object attached to the robot (As in our case where we use a circular-shaped Lidar) and then attempting to detect these robots by taking advantage of circularity and known diameter of these robots or objects attached to them.…”

Section: Robot Namementioning

confidence: 99%

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Simulation of Lidar-Based Robot Detection Task using ROS and Gazebo

Yilmaz¹,

Bayındır²

2019

European Journal of Science and Technology

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In the last few decades, the robotics world has seen great progress at all levels, from personal assistant robots to multi-robotic and intelligent swarm systems. Simulation platforms play a critical role in this improvement due to efficiency, flexibility, and fault tolerance they provide during the cycles of developing and testing new strategies and algorithms. In this paper, we model a new mobile robot equipped with a 2D Lidar using Robot Operating System (ROS) and use this robot model to develop a robot detection method in Gazebo simulation environment. Detecting surrounding objects and distinguishing robots from these objects (kin detection) are essential in multi-robot and swarm robotic applications. In this paper, we use Lidar to handle this task by applying the following steps: (1) acquisition of laser data and pre-processing, (2) segmentation of data using the point-distance-based segmentation method, (3) classification of segments by applying two levels of filtering: filtering by segment diameter which aims to eliminate segments that don't fit a certain size (Lidar size) using features for each segment, filtering by segment shape to check remaining segments to test if they fit the Lidar's shape (which is a circle with known radius) or not by using the circle fitting method and (4) identify the position of kin relative to the observer robot. Two different scenarios are discussed in the experiments section. In the first scenario, many cylindrical objects with radius different from the robot's Lidar were used in addition to a robot, and thus objects are distinguished from the robot in the first level of filtering without using the second one which may be a complex operation. In the second scenario, various objects with a similar radius were used, and due to the similarity in the radius between the Lidar and the objects, it was necessary to apply all the method's steps to detect the kin robots. It was noticed from experiments that the accuracy of the results depends on two main factors: the distance between the observer robot and other objects or robots and the amount of noise in the Lidar measurements.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Robot Namementioning

confidence: 99%

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Simulation of Lidar-Based Robot Detection Task using ROS and Gazebo

Yilmaz¹,

Bayındır²

2019

European Journal of Science and Technology

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“…A strategy for detecting circular objects based on their geometric properties and polar curve fitting is presented in [43]. To enhance this technique, support vector machines (SVMs) are employed for object detection, proving applicable to robot localization and navigation tasks.…”

Section: Related Workmentioning

confidence: 99%

Analytical Formalism for Data Representation and Object Detection with 2D LiDAR: Application in Mobile Robotics

Fagundes,

Caldeira,

Quemelli

et al. 2024

Sensors

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In mobile robotics, LASER scanners have a wide spectrum of indoor and outdoor applications, both in structured and unstructured environments, due to their accuracy and precision. Most works that use this sensor have their own data representation and their own case-specific modeling strategies, and no common formalism is adopted. To address this issue, this manuscript presents an analytical approach for the identification and localization of objects using 2D LiDARs. Our main contribution lies in formally defining LASER sensor measurements and their representation, the identification of objects, their main properties, and their location in a scene. We validate our proposal with experiments in generic semi-structured environments common in autonomous navigation, and we demonstrate its feasibility in multiple object detection and identification, strictly following its analytical representation. Finally, our proposal further encourages and facilitates the design, modeling, and implementation of other applications that use LASER scanners as a distance sensor.

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“…In [17], the proposed detection method is based on geometric property and polynomial fitting in polar coordinates instead of Cartesian coordinates. It is tailored for two-dimensional LIDAR (laser radar sensors) data.…”

Section: Heuristics For Circles Detection In 2d Imagesmentioning

confidence: 99%

Sphere Fitting with Applications to Machine Tracking

Epstein

Feldman

2020

Algorithms

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We suggest a provable and practical approximation algorithm for fitting a set P of n points in R d to a sphere. Here, a sphere is represented by its center x ∈ R d and radius r > 0 . The goal is to minimize the sum ∑ p ∈ P ∣ p − x − r ∣ of distances to the points up to a multiplicative factor of 1 ± ε , for a given constant ε > 0 , over every such r and x. Our main technical result is a data summarization of the input set, called coreset, that approximates the above sum of distances on the original (big) set P for every sphere. Then, an accurate sphere can be extracted quickly via an inefficient exhaustive search from the small coreset. Most articles focus mainly on sphere identification (e.g., circles in 2 D image) rather than finding the exact match (in the sense of extent measures), and do not provide approximation guarantees. We implement our algorithm and provide extensive experimental results on both synthetic and real-world data. We then combine our algorithm in a mechanical pressure control system whose main bottleneck is tracking a falling ball. Full open source is also provided.

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Circular Object Detection in Polar Coordinates for 2D LIDAR Data

Cited by 9 publications

References 13 publications

Simulation of Lidar-Based Robot Detection Task using ROS and Gazebo

Simulation of Lidar-Based Robot Detection Task using ROS and Gazebo

Analytical Formalism for Data Representation and Object Detection with 2D LiDAR: Application in Mobile Robotics

Sphere Fitting with Applications to Machine Tracking

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