A Cognitive Framework on Object Recognition and Localization for Robotic Vision

Batra, Varun; Jadon, Chahak; Kumar, Vijay

doi:10.1109/indo-taiwanican48429.2020.9181333

Cited by 4 publications

(2 citation statements)

References 15 publications

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“…With the advance of technology, mobile robots have become increasingly familiar, and the pursuit of enhanced localization accuracy through multi-sensor fusion has gained substantial attention across various fields [1]. In the domain of multi-sensor fusion, achieving high-quality real-time Simultaneous Localization and Mapping (SLAM) methods has emerged as a prominent research avenue.…”

Section: Introductionmentioning

confidence: 99%

LIF-M: A Manifold-Based Approach for 3D Robot Localization in Unstructured Environments

Zhang,

Liu,

2023

Applied Sciences

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Accurate localization of robots in unstructured environments poses challenges due to low localization accuracy and local trajectory oscillation caused by complex feature points when using Euclidean-based filtering methods. In this study, we propose a novel 3D robot localization method named LIF-M that leverages a manifold-based approach in conjunction with an unscented Kalman filter (UKF-M). Additionally, a relocalization algorithm is designed to ensure localization stability. The proposed method addresses the limitations of conventional Euclidean-based filtering methods by incorporating manifold-based techniques, providing a more comprehensive representation of the complex geometric features. We introduce the manifold concept, where the relevant definition is defined and utilized within the LIF-M framework. By combining left and right invariants, we effectively reduce noise uncertainty, resulting in improved localization accuracy. Moreover, we employ sigma points as a matrix representation of the state points’ space in order to seamlessly transition between the matrix space and the vector representation of the tangent space. Experimental tests and error calculations were conducted to evaluate the performance of various algorithm frameworks, and the results demonstrated the importance of the manifold-based approach for accurate attitude estimation. Compared to the standard UKF, the manifold space equips LIF-M with better robustness and stability in unstructured environments.

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Section: Introductionmentioning

confidence: 99%

LIF-M: A Manifold-Based Approach for 3D Robot Localization in Unstructured Environments

Zhang,

Liu,

2023

Applied Sciences

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“…The technique of color segmentation for object detection in real-time vision-based applications which is implemented through a stereoscopic system is briefly portrayed in [8]. While using the method of background subtraction with an aim of object recognition and localization elaborative discussion presented in [9,10]. Furthermore, the approach of deep neural networks is implemented while framing algorithm for object detection, localization and recognition are expressed in [11][12][13][14]24].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Object Detection and Localization for Vision-Based Robot Manipulator

Batra

Kumar

2021

SN COMPUT. SCI.

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Traditionally, the robot manipulators were assigned a monotonous task. Therefore, in an aim of simplifying the interactions between unstructured surrounding and robots, as well as to carry out the more complex operations, cognitive abilities such as visual perception are appended to systems for the sake of intelligent operations. Hence, this article directed towards the multistage process involved in the design and development of vision-based robot manipulator suitable for pick and place operation in real-time entailing external disturbances. The intact system development occurs in three phases: first, the robot manipulator with 3-DOF is designed, developed and also enhanced by integrating the vision source. Second, an algorithm for object detection and localization in real time is framed in which segmented object matrices are returned by the former and the position of the stabilized object is returned by the latter. Third, entire hardware-software integration is achieved to perform the desired operation. The algorithm developed in this paper proves to be good for the developed vision-based manipulator, as we achieve quite a good accuracy in object detection algorithm whereas 98.41-99.12% accuracy is achieved in the object localization algorithm. Keywords Object detection • Object localization • Real-time robot manipulatorThis article is part of the topical collection "Applications of Cloud Computing, Data Analytics and Building Secure Networks" guestedited by Rajnish Sharma, Pao-Ann Hsiung and Sagar Juneja".

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