Abstract
Outdoor mobile robot applications generally implement Global Positioning Systems (GPS) for localization tasks. However, GPS accuracy in outdoor localization has less accuracy in different environmental conditions. This paper presents two outdoor localization methods based on deep learning and landmark detection. The first localization method is based on the Faster Regional-Convolutional Neural Network (Faster R-CNN) landmark detection in the captured image. Then, a feedforward neural network (FFNN) is trained to determine robot location coordinates and compass orientation from detected landmarks. The second localization employs a single convolutional neural network (CNN) to determine location and compass orientation from the whole image. The dataset consists of images, geolocation data and labeled bounding boxes to train and test two proposed localization methods. Results are illustrated with absolute errors from the comparisons between localization results and reference geolocation data in the dataset. The experimental results pointed both presented localization methods to be promising alternatives to GPS for outdoor localization.
Highlights
In present world, mobile robots operate in various fields of applications, such as logistics, medical, agriculture, health caring and housekeeping
Global Positioning Systems (GPS) is the method that has been widely applied among a variety of outdoor applications, some of which are: the mobile robot for high-voltage transmission line inspection [2], the autonomous position control of multiple aerial vehicles [3], the mobile robot for gas level mapping [4], the navigation system for mobile robots using GPS and inertial navigation system (INS) [5], and map building with the simultaneous localization and mapping (SLAM) for firefighter robots [6]
We investigated the object detection capabilities of Convolutional neural network (CNN) and one of its successors, Faster R-CNN, to be used for localization detection capabilities of CNN and one of its successors, Faster R-CNN, to be used for localization based based on visual landmarks
Summary
Mobile robots operate in various fields of applications, such as logistics, medical, agriculture, health caring and housekeeping. Success in navigation requires success of different factors, including localization, in which the robots must be able to determine their positions in the environments [1]. Recent findings suggested a significant number of localization methods for both outdoor and indoor environments. Global Positioning Systems (GPS) is the method that has been widely applied among a variety of outdoor applications, some of which are: the mobile robot for high-voltage transmission line inspection [2], the autonomous position control of multiple aerial vehicles [3], the mobile robot for gas level mapping [4], the navigation system for mobile robots using GPS and inertial navigation system (INS) [5], and map building with the simultaneous localization and mapping (SLAM) for firefighter robots [6].
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