Visual SLAM for robot navigation in healthcare facility

Fang, Baofu; Mei, Gaofei; Yuan, Xiaohui; Wang, Le; Wang, Zaijun; Wang, Junyang

doi:10.1016/j.patcog.2021.107822

Cited by 70 publications

(36 citation statements)

References 38 publications

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“…The current position and speed of wheelchair are calculated according to the encoder values of the two driving wheels, and the trajectory is converted into the world coordinate system. The trajectory is published to ROS by SPI (Serial Peripheral Interface) communication to provide data for SLAM (Simultaneous Localization and Mapping) algorithm [28]. At the same time, the yaw angle of the wheelchair in the process of moving is recorded, and the positioning accuracy of the We conducted docking experiments in an 80m 2 laboratory, where the laboratory light environment was well.…”

Section: Resultsmentioning

confidence: 99%

Wheelchair Automatic Docking Method for Body-Separated Nursing Bed Based on Grid Map

Zhu

Meng

et al. 2021

IEEE Access

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To improve the mode and precision of wheelchair/nursing-bed automatic docking, a novel central embedded wheelchair/nursing-bed automatic docking method based on grid map is proposed. Firstly, Laplace operator and Iterative Closest Point (ICP) algorithm are used to filter and match Lidar point cloud, and the linear features of V-shaped artificial landmark are fitted by Split-merge method and least square method. Then Extended Kalman Filter (EKF) is used to fuse Inertial Measurement Unit (IMU) and odometer data to realize the localization of the bed and wheelchair. Meanwhile, the grid map is used for path planning. Based on the center-line of the two rear wheels and the angular bisector of V-shaped artificial landmark, the wheelchair pose is adjusted in real-time to ensure that the wheelchair gradually approaches the bed along the angular bisector of V-shaped artificial landmark. The yaw angle is reduced by using the improved Proportion Integration Differentiation (PID). 9 sets of experimental data, ie. (x, y,  ) were collected at different starting positions during the docking process. The results show that the yaw angle of the wheelchair during the docking process is controlled within 2.5°, and the distance deviation between the final position and the ideal position of the wheelchair after docking is controlled within 0.02m. In the case of light interference with different luminous fluxes, the docking can still maintain good performance. The proposed docking algorithm has the robust performance of rapid response and low steady error, which can greatly improves the self-care ability of the bedridden elderly, and reduces the labor intensity of the nursing staff.

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Section: Resultsmentioning

confidence: 99%

Wheelchair Automatic Docking Method for Body-Separated Nursing Bed Based on Grid Map

Zhu

Meng

et al. 2021

IEEE Access

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“…In recent years, with the development of artificial intelligence and machine vision, more and more researchers choose to use visual SLAM-(simultaneous localization and mapping-) based [13] robot indoor positioning and navigation technology. For example, in literature [14], robots are applied to hospitals, and a SLAM technology based on RGB and depth images is proposed to improve hospital operation efficiency. Since most of the current visual SLAM researches assume that the environment is static, it will be a challenge when dealing with real and dynamic scenes.…”

Section: Related Workmentioning

confidence: 99%

“…Since most of the current visual SLAM researches assume that the environment is static, it will be a challenge when dealing with real and dynamic scenes. Based on this problem, literature [14] also proposed a method that effectively deals with SLAM problems for scenarios with dynamic objects, e.g., people and movable objects, based on the semantic descriptor extracted from images with help of a knowledge graph. Literature [15] studied real-time robot detection and map construction.…”

Section: Related Workmentioning

confidence: 99%

Robot Indoor Positioning and Navigation Based on Improved WiFi Location Fingerprint Positioning Algorithm

Peng

2022

Wireless Communications and Mobile Computing

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In response to the traditional WiFi location fingerprint positioning algorithm still having a low positioning accuracy, which is difficult to meet the robot indoor positioning and navigation needs, a series of improvements are made to the traditional WiFi location fingerprint positioning algorithm, so that the positioning accuracy of the algorithm can be effectively improved. At the stage of building the location fingerprint library offline, WiFi signals are collected at each reference point by reducing the reference point spacing of the traditional location fingerprinting algorithm and then using different time period collection methods. The WiFi signal strength values are standardized using the standardization processing method to improve the specificity of traditional location fingerprinting. In the real-time localization stage, the WiFi signals collected from the unknown location points are averaged, and then, the fingerprint similarity calculation is performed using a matching method based on the magnitude of the Marxian distance as a similarity reference. In order to eliminate the location fingerprints that degrade the localization accuracy, an improved adaptive K -value WKNN algorithm is integrated at the end of the localization algorithm. The improved localization algorithm and the proposed raster-based navigation algorithm are validated in a fixed experimental environment. The experimental results show that the probability of the improved algorithm’s positioning error within 0.4 m is 49%, which is a 35% improvement over the conventional algorithm. Combining the improved positioning algorithm with our proposed grid-based navigation algorithm, the final navigation error probability within 0.8 m is 62%.

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“…presented a novel SLAM algorithm using RGB and depth images to reduce cross-infection risk between doctors and patients. This method could also raise hospital operation efficiency [1] . Dash et al.…”

Section: Introductionmentioning

confidence: 98%

An evolvable adversarial network with gradient penalty for COVID-19 infection segmentation

Zhu

Zhang

et al. 2021

Applied Soft Computing

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COVID-19 infection segmentation has essential applications in determining the severity of a COVID-19 patient and can provide a necessary basis for doctors to adopt a treatment scheme. However, in clinical applications, infection segmentation is performed by human beings, which is time-consuming and generally introduces bias. In this paper, we developed a novel evolvable adversarial framework for COVID-19 infection segmentation. Three generator networks compose an evolutionary population to accommodate the current discriminator, i.e., generator networks evolved with different mutations instead of the single adversarial objective to provide sufficient gradient feedback Compared with the existing work that enforces a Lipschitz constraint by weight clipping, which may lead to gradient exploding or vanishing, the proposed model also incorporates the gradient penalty into the network, penalizing the discriminator’s gradient norm input. Experiments on several COVID-19 CT scan datasets verified that the proposed method achieved superior effectiveness and stability for COVID-19 infection segmentation.

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Visual SLAM for robot navigation in healthcare facility

Cited by 70 publications

References 38 publications

Wheelchair Automatic Docking Method for Body-Separated Nursing Bed Based on Grid Map

Wheelchair Automatic Docking Method for Body-Separated Nursing Bed Based on Grid Map

Robot Indoor Positioning and Navigation Based on Improved WiFi Location Fingerprint Positioning Algorithm

An evolvable adversarial network with gradient penalty for COVID-19 infection segmentation

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