A Robot Localization Framework Using CNNs for Object Detection and Pose Estimation
This addresses localization for small or cost-efficient robots in swarm robotics, representing an incremental improvement with specific gains.
The paper tackles robot localization in indoor environments by introducing a two-stage CNN framework for object detection and pose estimation, achieving up to 98% mAP@IOU0.5 and 1.6° orientation error at 50 Hz.
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.