A low-cost visual navigation and mapping system for Unmanned Aerial Vehicle using LSD-SLAM algorithm

Anandharaman, S.; Sudhakaran, M.; Seyezhai, R.

doi:10.1109/get.2016.7916802

Cited by 3 publications

(2 citation statements)

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“…On the other hand, given that the accuracy of SLAM is based on the precision of sensors, and the precision is sometimes related to the price of sensors, several researches are focus on how to cost down patrol systems [10][11][12][13][14][15][16][17][18][19][20][21][22][23]. Jiang, G. et al propose a graph optimization-based SLAM framework through combining a low-cost LiDAR and an RGB-D camera to improve the defect of accumulated error for building large maps only by low-cost LiDAR [10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

“…For more, Gong Z. et al combine a cheap LiDAR system and crash sensors with a NVIDIA Jetson tk1 module on a wheeling robot to fabricate a low cost patrolling platform, and using tiny SLAM algorithm on the platform to achieve a real-time indoor mapping [18]. Anandharaman S. et al use a monocular camera with large-scale direct monocular SLAM (LSD-SLAM) method on unmanned aerial vehicle to realize a low-cost navigation and mapping system [19]. Xi W. et al use RPLiDAR on iRobot with Lenovo G470 laptop to build an experimental facility.…”

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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