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Processing of LiDAR and IMU data for target detection and odometry of a mobile robot

Giannoccaro Nicola Ivan, Nishida Takeshi, Lay-Ekuakille Aimé, Velazquez Ramiro, Visconti Paolo

Journal of Automation Mobile Robotics and Intelligent Systems

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2022

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Vol. 16, No. 1

3--13

EN

In this paper, the processing of the data of a 3D light detection and distance measurement (LiDAR) sensor mounted on a mobile robot is demonstrated, introducing an innovative methodology to manage the data and extract useful information. The LiDAR sensor is placed on a mobile robot which has a modular design that permits the easy change of the number of wheels, was designed to travel through several environments, and saves energy by changing the number and arrangement of the wheels in each environment. In addition, the robot can recognize landmarks in a structured environment by using a classification technique on each frame acquired by the LiDAR. Furthermore, considering the experimental tests, a new simple algorithm based on the LiDAR data processing together with the inertial data (IMU sensor) through a Kalman filter is proposed to characterize the robot’s pose by the surrounding environment with fixed landmarks. Finally, the limits of the proposed algorithm have been analyzed, highlighting new improvements in the future prospective development for permitting autonomous navigation and environment perception with a simple, modular, and low-cost device.

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3D object detection and recognition for robotic grasping based on RGB-D images and global features

Czajewski W., Kołomyjec K.

Foundations of Computing and Decision Sciences

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2017

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Vol. 42, No. 3

220--237

EN

This paper describes the results of experiments on detection and recognition of 3D objects in RGB-D images provided by the Microsoft Kinect sensor. While the studies focus on single image use, sequences of frames are also considered and evaluated. Observed objects are categorized based on both geometrical and visual cues, but the emphasis is laid on the performance of the point cloud matching method. To this end, a rarely used approach consisting of independent VFH and CRH descriptors matching, followed by ICP and HV algorithms from the Point Cloud Library is applied. Successfully recognized objects are then subjected to a classical 2D analysis based on color histogram comparison exclusively with objects in the same geometrical category. The proposed two-stage approach allows to distinguish objects of similar geometry and different visual appearance, like soda cans of various brands. By separating geometry and color identification phases, the applied system is still able to categorize objects based on their geometry, even if there is no color match. The recognized objects are then localized in the three-dimensional space and autonomously grasped by a manipulator. To evaluate this approach, a special validation set was created, and additionally a selected scene from the Washington RGB-D Object Dataset was used.

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Significance of features in object recognition using depth sensors

Harasymowicz-Boggio B., Chechlinski L, Siemiatkowska B.

Optica Applicata

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2015

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Vol. 45, nr 4

559--571

EN

This article concerns a key topic in the field of visual object recognition – the use of features. Object recognition algorithms typically rely on a fixed vector of pre-selected features extracted from 2D or 3D scenes, which are then analyzed with various classification techniques. On the other hand, the activation of particular features in biological vision systems is hierarchical and data-driven. To achieve a deeper understanding of the subject, we have introduced several mathematical tools to estimate multiple RGB-D features’ relevance for different object recognition tasks and conducted statistical experiments involving our database of high quality 3D point clouds. From the thorough analysis of the obtained results we draw conclusions that may be useful to design better, more adaptive object recognition algorithms.

4

Rubik's cube reconstruction from single view for service robots

Kasprzak W., Szynkiewicz W., Czajka Ł.

Machine Graphics and Vision

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2006

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Vol. 15, No. 3/4

451-459

EN

The Rubik's cube puzzle is seen as a benchmark for service robots. In such an application, a computer vision subsystem is required to locate the object in space and to determine the configuration of its colored cells. This paper presents a robust algorithm for Rubik's cube reconstruction from a single view in real time. An issue of special interest is to obtain a good tade-off between the quality of results, and the computational complexity of the algorithm.