Research into Kinect/Inertial Measurement Units Based on Indoor Robots

Li, Huixia; Xiao, Wen; Guo, Hang; Yu, Min

doi:10.3390/s18030839

Cited by 11 publications

(5 citation statements)

References 13 publications

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“…Palmieri [34] also adopts a Kalman filter to make the velocity values of human arms more accurate. Similar to previous research, the Kalman filter is used to obtain the errors of the position and attitude outputs [35]. Some scholars [36] combine it with the multi-frame average method to solve the problem of image edge accuracy.…”

Section: B Data Compensation Correction Algorithmmentioning

confidence: 99%

Research on Skeleton Data Compensation of Gymnastics based on Dynamic and Static Two-dimensional Regression using Kinect

Zhao

Zan

Chen

2022

Measurement Science Review

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The intelligent training and assessment of gymnastics movements require studying motion trajectory and reconstructing the character animation. Microsoft Kinect has been widely used due to its advantages of low price and high frame rate. However, its optical characteristics are inevitably affected by illumination and occlusion. It is necessary to reduce data noise via specific algorithms. Most of the existing research focuses on local motion but lacks consideration of the whole human skeleton. Based on the analysis of the spatial characteristics of gymnastics and the movement principle of the human body, this paper proposes a dynamic and static two-dimensional regression compensation algorithm. Firstly, the constraint characteristics of human skeleton motion were analyzed, and the maximum constraint table and Mesh Collider were established. Then, the dynamic acceleration of skeleton motion and the spatial characteristics of static limb motion were calculated based on the data of adjacent effective skeleton frames before and after the collision. Finally, using the least squares polynomial fitting to compensate and correct the lost skeleton coordinate data, it realizes the smoothness and rationality of human skeleton animation. The results of two experiments showed that the solution of the skeleton point solved the problem caused by data loss due to the Kinect optical occlusion. The data compensation time of an effective block skeleton point can reach 180 ms, with an average error of about 0.1 mm, which shows a better data compensation effect of motion data acquisition and animation reconstruction.

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Section: B Data Compensation Correction Algorithmmentioning

confidence: 99%

Research on Skeleton Data Compensation of Gymnastics based on Dynamic and Static Two-dimensional Regression using Kinect

Zhao

Zan

Chen

2022

Measurement Science Review

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“…This indicates that the filter estimates the error of the designated attitude position, and velocity of IMU, it continuously estimates the output error of various sensors to optimize the information of each sensor. 22…”

Section: Kf-based Lidar/imu-integrated Navigationmentioning

confidence: 99%

Light detection and ranging/inertial measurement unit-integrated navigation positioning for indoor mobile robots

Xiaoyi

Guo

et al. 2020

International Journal of Advanced Robotic Systems

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To overcome the problem of the low accuracy and large accumulated errors of indoor mobile navigation and positioning, a method to integrate the light detection and ranging-and inertial measurement unit-based measurement is proposed. Firstly, the voxel-scale-invariant feature transform feature extraction algorithm for light detection and ranging is studied. Then, the errors of light detection and ranging measurement due to the change of the scan plane compensated based on aiding information from inertial measurement unit. The relative position parameters and the differences of measurements of light detection and ranging at adjacent times are used to estimate the error of inertial measurement unit sensors by a Kalman filter. Several experiments are carried out in the indoor corridor and the results demonstrate that precision of the light detection and ranging/inertial measurement unit-integrated indoor mobile robot localization is higher than that of single light detection and ranging sensor.

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“…Thus, MLS's trajectory in building interior is estimated by inversely calculating from the moving direction and distance obtained by the inertial measurement unit [27][28][29]. The approximate trajectory causes the error in detecting building components [30,31]. If the number of scanning positions increases, it has limitations on applying the existing methods to PCD without additional sensors because the accumulated error in PCD causes confusion of detecting the existence of building components based on the existing method suitable for high-quality PCD [32][33][34].…”

Section: Introductionmentioning

confidence: 99%

Building Component Detection on Unstructured 3D Indoor Point Clouds Using RANSAC-Based Region Growing

Lee

Kim

et al. 2021

Remote Sensing

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With the advancement of light detection and ranging (LiDAR) technology, the mobile laser scanner (MLS) has been regarded as an important technology to collect geometric representations of the indoor environment. In particular, methods for detecting indoor objects from indoor point cloud data (PCD) captured through MLS have thus far been developed based on the trajectory of MLS. However, the existing methods have a limitation on applying to an indoor environment where the building components made by concrete impede obtaining the information of trajectory. Thus, this study aims to propose a building component detection algorithm for MLS-based indoor PCD without trajectory using random sample consensus (RANSAC)-based region growth. The proposed algorithm used the RANSAC and region growing to overcome the low accuracy and uniformity of MLS caused by the movement of LiDAR. This study ensures over 90% precision, recall, and proper segmentation rate of building component detection by testing the algorithm using the indoor PCD. The result of the case study shows that the proposed algorithm opens the possibility of accurately detecting interior objects from indoor PCD without trajectory information of MLS.

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Research into Kinect/Inertial Measurement Units Based on Indoor Robots

Cited by 11 publications

References 13 publications

Research on Skeleton Data Compensation of Gymnastics based on Dynamic and Static Two-dimensional Regression using Kinect

Research on Skeleton Data Compensation of Gymnastics based on Dynamic and Static Two-dimensional Regression using Kinect

Light detection and ranging/inertial measurement unit-integrated navigation positioning for indoor mobile robots

Building Component Detection on Unstructured 3D Indoor Point Clouds Using RANSAC-Based Region Growing

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