Keeping Less is More: Point Sparsification for Visual SLAM
Yeonsoo Park,Soohyun Bae,Yeonsoo Park,Soohyun Bae
When adapting Simultaneous Mapping and Localization (SLAM) to real-world applications, such as autonomous vehicles, drones, and augmented reality devices, its memory footprint and computing cost are the two main factors limiting the performance and the range of applications. In sparse feature based SLAM algorithms, one efficient way for this problem is to limit the map point size by selecting the ...
