Vision based robotic person following under lighting variations

Tarokh, Mahmoud; Merloti, Paulo; Duddy, J.; Lee, M.

doi:10.1109/icsenst.2008.4757090

Cited by 5 publications

(3 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The governing equation is a linear function of the gap for longitudinal control. If a camera is applied to obtain the LP's position, the gap between PFR and LP is noted by the number of the LP's pixels on the photosensitive sensors (Tarokh et al, 2008). When the gap increases, the number of pixels of the LP decreases and the speed increases according to the control strategy.…”

Section: Introductionmentioning

confidence: 99%

Longitudinal control for person-following robots

Wang

et al. 2022

JICV

View full text Add to dashboard Cite

Purpose This paper aims to address the longitudinal control problem for person-following robots (PFRs) for the implementation of this technology. Design/methodology/approach Nine representative car-following models are analyzed from PFRs application and the linear model and optimal velocity model/full velocity difference model are qualified and selected in the PFR control. Findings A lab PFR with the bar-laser-perception device is developed and tested in the field, and the results indicate that the proposed models perform well in normal person-following scenarios. Originality/value This study fills a gap in the research on PRFs longitudinal control and provides a useful and practical reference on PFRs longitudinal control for the related research.

show abstract

Section: Introductionmentioning

confidence: 99%

Longitudinal control for person-following robots

Wang

et al. 2022

JICV

View full text Add to dashboard Cite

show abstract

“…Low power consumption and low weight make it possible to deploy vision sensors on mobile platforms with limited load. Vision SLAM has a wide range of applications both indoors and outdoors [ 4 ]. The image information obtained from the vision sensors is used to calculate the camera motion between adjacent images and thus estimate the camera motion trajectory.…”

Section: Introductionmentioning

confidence: 99%

A Coupled Visual and Inertial Measurement Units Method for Locating and Mapping in Coal Mine Tunnel

Zhu

et al. 2022

Sensors

View full text Add to dashboard Cite

Mobile robots moving fast or in scenes with poor lighting conditions often cause the loss of visual feature tracking. In coal mine tunnels, the ground is often bumpy and the lighting is uneven. During the movement of the mobile robot in this scene, there will be violent bumps. The localization technology through visual features is greatly affected by the illumination and the speed of the camera movement. To solve the localization and mapping problem in an environment similar to underground coal mine tunnels, we improve a localization and mapping algorithm based on a monocular camera and an Inertial Measurement Unit (IMU). A feature-matching method that combines point and line features is designed to improve the robustness of the algorithm in the presence of degraded scene structure and insufficient illumination. The tightly coupled method is used to establish visual feature constraints and IMU pre-integration constraints. A keyframe nonlinear optimization algorithm based on sliding windows is used to accomplish state estimation. Extensive simulations and practical environment verification show that the improved simultaneous localization and mapping (SLAM) system with a monocular camera and IMU fusion can achieve accurate autonomous localization and map construction in scenes with insufficient light such as coal mine tunnels.

show abstract

“…However, to the author's knowledge, many works have focused on the problem of target detection and tracking but rarely addressed the problem of designing a suitable controller for driving a robot [15]. The existing controller mainly includes the PID controller [16], visual based sliding mode controller [15], fuzzy based controller [17], and intelligent controller [7]. Ouadah et al [15] presented two sliding mode controllers to control the robot according to the person's position obtained from RFID system and visual system, respectively.…”

Section: Introductionmentioning

confidence: 99%

Person Tracking System by Fusing Multicues Based on Patches

Jia

Wang

et al. 2015

Journal of Sensors

View full text Add to dashboard Cite

A person tracking algorithm by fusing multicues based on patches is proposed to solve the problem of distinguishing person, occlusion, and illumination variations. Kinect is mounted on the robot for providing color images and depth maps. A detector representing a person by using the fusion of multicues based on patches is proposed. The detector divides the person into many patches and then represents each patch by using depth-color histograms and depth-texture histograms. The appearance representation, considering depth, color, and texture information, has powerful discrimination ability to handle the problems of occlusion, illumination changes, and pose variations. Considering the motion of the robot and person, a tracker called motion extended Kalman filter (MEKF) is presented to predict the person’s position. The result of the tracker is treated as a candidate sample of the detector, and then the result of the detector is the previous knowledge of the tracker. The detector and tracker supplement each other and improve the tracking performance. To drive the robot towards the given person precisely, a fuzzy based intelligent gear control strategy (FZ-IGS) is implemented. Experiments demonstrate that the proposed approach can track a person in a complex environment and have an optimum performance.

show abstract

Vision based robotic person following under lighting variations

Cited by 5 publications

References 9 publications

Longitudinal control for person-following robots

Longitudinal control for person-following robots

A Coupled Visual and Inertial Measurement Units Method for Locating and Mapping in Coal Mine Tunnel

Person Tracking System by Fusing Multicues Based on Patches

Contact Info

Product

Resources

About