A Monocular Vision Sensor-Based Efficient SLAM Method for Indoor Service Robots

Lee, Tae-jae; Kim, Chul-hong; Cho, Dong‐il Dan

doi:10.1109/tie.2018.2826471

Cited by 102 publications

(46 citation statements)

References 38 publications

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“…The patrol system is highly dependent on the function of simultaneous localization and mapping (SLAM), and it also needs rapid SLAM execution efficiency. Therefore, much research has been devoted to improving the working efficiency of SLAM [1][2][3][4][5][6]. Lee, C.W.…”

Section: Introductionmentioning

confidence: 99%

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Development of a Low Cost and Path-free Autonomous Patrol System Based on Stereo Vision System and Checking Flags

Lan

Chang

2020

Applied Sciences

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Nowadays, security guard patrol services are becoming roboticized. However, high construction prices and complex systems make patrol robots difficult to be popularized. In this research, a simplified autonomous patrolling robot is proposed, which is fabricated by upgrading a wheeling household robot with stereo vision system (SVS), radio frequency identification (RFID) module, and laptop. The robot has four functions: independent patrolling without path planning, checking, intruder detection, and wireless backup. At first, depth information of the environment is analyzed through SVS to find a passable path for independent patrolling. Moreover, the checkpoints made with RFID tag and color pattern are placed in appropriate positions within a guard area. While a color pattern is detected by the SVS, the patrolling robot is guided to approach the pattern and check its RFID tag. For more, the human identification function of SVS is used to detect an intruder. While a skeleton information of the human is analyzed by SVS, the intruder detection function is triggered, then the robot follows the intruder and record the images of the intruder. The recorded images are transmitted to a server through Wi-Fi to realize the remote backup, and users can query the recorded images from the network. Finally, an experiment is made to test the functions of the autonomous patrolling robot successfully.

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Section: Introductionmentioning

confidence: 99%

“…[2]. Li, X. et al proposed a monocular vision fast 3D SLAM system by using a fusion of localization, mapping, and parses scenes [3,4]. In addition, Valiente, D. et al also used an omnidirectional monocular sensor to improve the stability of SLAM [5].…”

Section: Introductionmentioning

confidence: 99%

Development of a Low Cost and Path-free Autonomous Patrol System Based on Stereo Vision System and Checking Flags

Lan

Chang

2020

Applied Sciences

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“…However, they have significant disadvantages when used individually: the metric scale of stereo camera can be obtained directly by using fixed baseline length, but it can only be estimated accurately in a limited depth range [3]; lidar has high precision in indoor, but it will encounter the reflection problem of glass surface in outdoor [4]; cheap IMUs are extremely susceptible to bias and noise [5]; monocular camera cannot estimate the absolute metric scale [6]. This paper mainly studies the monocular vision-inertial navigation system (VINS) based on multi-sensor fusion [7], which has the advantages of small size, lightweight, observable scale, roll, and pitch angle, etc.…”

Section: Introductionmentioning

confidence: 99%

“…Stereo DSO is an improved version of DSO, which can realize the real-time estimation of motion and 3D structure with high accuracy and robustness on the moving stereo camera [3]. Lee authors in [7] presents a new implementation method for efficient simultaneous localization and mapping using a forward-viewing monocular vision sensor. The method is developed to be applicable in real time on a low-cost embedded system for indoor service robots.…”

Section: Introductionmentioning

confidence: 99%

SD-VIS: A Fast and Accurate Semi-Direct Monocular Visual-Inertial Simultaneous Localization and Mapping (SLAM)

Liu

Wang

2020

Sensors

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In practical applications, how to achieve a perfect balance between high accuracy and computational efficiency can be the main challenge for simultaneous localization and mapping (SLAM). To solve this challenge, we propose SD-VIS, a novel fast and accurate semi-direct visual-inertial SLAM framework, which can estimate camera motion and structure of surrounding sparse scenes. In the initialization procedure, we align the pre-integrated IMU measurements and visual images and calibrate out the metric scale, initial velocity, gravity vector, and gyroscope bias by using multiple view geometry (MVG) theory based on the feature-based method. At the front-end, keyframes are tracked by feature-based method and used for back-end optimization and loop closure detection, while non-keyframes are utilized for fast-tracking by direct method. This strategy makes the system not only have the better real-time performance of direct method, but also have high accuracy and loop closing detection ability based on feature-based method. At the back-end, we propose a sliding window-based tightly-coupled optimization framework, which can get more accurate state estimation by minimizing the visual and IMU measurement errors. In order to limit the computational complexity, we adopt the marginalization strategy to fix the number of keyframes in the sliding window. Experimental evaluation on EuRoC dataset demonstrates the feasibility and superior real-time performance of SD-VIS. Compared with state-of-the-art SLAM systems, we can achieve a better balance between accuracy and speed.

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“…Inductive proximity transducers [ 4 , 5 , 6 ] are widely used in high-precision displacement inspection fields with the advantages of compact volume, high precision (in micron scale) and fast response. For highly precise and fast shape measurement, industrial photogrammetry [ 7 , 8 , 9 ], with the measurement characteristics of both a high precision of up to ±(8 μm + 8 μm/m) and high efficiency, is more commonly used.…”

Section: Introductionmentioning

confidence: 99%

A Displacement Field Perception Method for Component Digital Twin in Aircraft Assembly

Li¹,

Liu²,

Liu³

et al. 2020

Sensors

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Full-field displacement perception and digital twins for core components play a crucial role in the precision manufacturing industry, such as aviation manufacturing. This paper presents a real-time full-field displacement perception method for the combination of online multipoint displacement monitoring and matrix completion theory. Firstly, a conceptual full-field displacement perception model based on the observed information of the multi-points is established. To obtain the full-field displacements of a core component, the component is divided into plentiful discrete points, including observed and unobserved points, based on which the relationship between the observed points and the full-field displacements is established. Then, the solution method of the full-field displacement perception model is proposed. Based on the matrix completion principle and the big data of the simulation, the optimization problem is employed to work out the model and, meanwhile, the pseudo-code is put forward. Finally, the full-field displacement perception experiments are performed. Repeated experiments show that the max error of the displacements calculated by the proposed method can be less than 0.094 mm and the median error can be less than 0.054 mm, while the average time frame can be less than 0.48 s, which is promising considering the high precision and efficiency requirements of the assembly of large aircraft.

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A Monocular Vision Sensor-Based Efficient SLAM Method for Indoor Service Robots

Cited by 102 publications

References 38 publications

Development of a Low Cost and Path-free Autonomous Patrol System Based on Stereo Vision System and Checking Flags

Development of a Low Cost and Path-free Autonomous Patrol System Based on Stereo Vision System and Checking Flags

SD-VIS: A Fast and Accurate Semi-Direct Monocular Visual-Inertial Simultaneous Localization and Mapping (SLAM)

A Displacement Field Perception Method for Component Digital Twin in Aircraft Assembly

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