Nowadays, SLAM (Simultaneous Localization and Mapping) is considered by the Robotics community to be a mature field.
In this work, we propose a unified methodology to design and develop efficient Least-Squares Optimization algorithms, focusing on the structures and patterns of each specific domain.
In this paper we propose an approach to embed continuous and selector cues in binary feature descriptors used for visual place recognition.
Reliable and efficient Visual Place Recognition is a major building block of modern SLAM systems.
Our approach exploits the different cues in a natural and consistent way and the registration can be done at framerate for a typical range or imaging sensor.
We compare the classification results obtained using both real and synthetic images as training data.