Real-time topological localization using structured-view ConvNet with expectation rules and training renewal

• Published in: Robotics and autonomous systems 2020-09, Vol.131, p.103578, Article 103578
• Main Authors: Li, Chih-Hung G., Hong, Yi-Feng, Hsu, Po-Kai, Maneewarn, Thavida
• Format: Article
• Language: English
• ISSN: 0921-8890; 1872-793X
• DOI: 10.1016/j.robot.2020.103578

Mobile service robots possess high potential of providing numerous assistances in the working areas. In an attempt to develop a mobile service robot which is dynamically balanced for faster movement and taller manipulation capability, we designed and prototyped J4.alpha, which is intended for swift navigation and nimble manipulation. Previously, we devised a pure visual method based on a supervised deep learning model for real-time recognition of nodal locations. Four low-resolution RGB cameras are installed around J4.alpha to capture the surrounding visual features for training and detection. As the method is developed for ease of implementation, fast real-time application, accurate detection, and low cost, we further improve the accuracy and the practicality of the method in this study. Specifically, a set of expectation rules are introduced to reject outlier detections, and a scheme of training renewal is devised to effectively react to environmental modifications. In our previous tests, precision and recall rates of the location coordinate detection by the ConvNet models were generally between 0.78 and 0.91; by introducing the expectation rules, precision and recall are improved by approximately 10%. A large scale field test is also carried out here for both corridor and factory scenarios; the performance of the proposed method was tested for detection accuracy and verified for 2 m and 0.5 m nodal intervals. The scheme of training renewal designed for capturing and reflecting environmental modifications was also proved to be effective. •A collaged image deep learning is developed for real-time robot localization.•Surrounding views are automatically structured and annotated for training.•ConvNet accuracy of nodal location recognition is between 80% and 90%.•Expectation rules enhance precision and recall to 90%–100%.•Training renewal scheme captures and reflects environmental modifications.