Object-Oriented and Visual-Based Localization in Urban Environments.

In visual-based localization, prior research falls short in addressing challenges for the Internet of Things with limited computational resources. The dominant state-of-the-art models are based on separate feature extractors and descriptors without consideration of the constraints of small hardware, the issue of inconsistent image scale, or the presence of multi-objects. We introduce "OOPose", a real-time object-oriented pose estimation framework that leverages dense features from off-the-shelf object detection neural networks.