Visual Place Recognition based on Multi-level CNN Features

Bao, Zhenqiang; Li, Aihua; Cui, Zhigao; Zhang, Jinming

doi:10.1145/3265639.3265684

Cited by 1 publication

(1 citation statement)

References 14 publications

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“…Instead of traditional computer vision based techniques, learning based environmental perception (e.g., Deep Learning) is a new paradigm. The deep learning neural network layers can learn features to distinguish and determine objects at high accuracy and can be used in various vision tasks such as outdoor environmental perception for autonomous vehicles [ 18 , 19 , 20 ], cleaning and maintenance robot vision pipeline [ 2 , 21 , 22 , 23 , 24 ], mobile robot place recognition and mapping tasks [ 25 , 26 , 27 , 28 ]. While these state-of-the-art deep learning algorithms have achieved impressive results for environment perception design, there are some shortcomings such as false detection, which can be due to objects with similar features.…”

Section: Introductionmentioning

confidence: 99%

sTetro-Deep Learning Powered Staircase Cleaning and Maintenance Reconfigurable Robot

Ramalingam

Mohan

Balakrishnan

et al. 2021

Sensors

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Staircase cleaning is a crucial and time-consuming task for maintenance of multistory apartments and commercial buildings. There are many commercially available autonomous cleaning robots in the market for building maintenance, but few of them are designed for staircase cleaning. A key challenge for automating staircase cleaning robots involves the design of Environmental Perception Systems (EPS), which assist the robot in determining and navigating staircases. This system also recognizes obstacles and debris for safe navigation and efficient cleaning while climbing the staircase. This work proposes an operational framework leveraging the vision based EPS for the modular re-configurable maintenance robot, called sTetro. The proposed system uses an SSD MobileNet real-time object detection model to recognize staircases, obstacles and debris. Furthermore, the model filters out false detection of staircases by fusion of depth information through the use of a MobileNet and SVM. The system uses a contour detection algorithm to localize the first step of the staircase and depth clustering scheme for obstacle and debris localization. The framework has been deployed on the sTetro robot using the Jetson Nano hardware from NVIDIA and tested with multistory staircases. The experimental results show that the entire framework takes an average of 310 ms to run and achieves an accuracy of 94.32% for staircase recognition tasks and 93.81% accuracy for obstacle and debris detection tasks during real operation of the robot.

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