Title: A Robot Localization Framework Using CNNs for Object Detection and Pose Estimation,
Abstract: External localization is an essential part for the indoor operation of small
or cost-efficient robots, as they are used, for example, in swarm robotics. We
introduce a two-stage localization and instance identification framework for
arbitrary robots based on convolutional neural networks. Object detection is
performed on an external camera image of the operation zone providing robot
bounding boxes for an identification and orientation estimation convolutional
neural network. Additionally, we propose a process to generate the necessary
training data. The framework was evaluated with 3 different robot types and
various identification patterns. We have analyzed the main framework
hyperparameters providing recommendations for the framework operation settings.
We achieved up to 98% mAP@IOU0.5 and only 1.6° orientation error, running
with a frame rate of 50 Hz on a GPU.