Geometrical and statistical incremental semantic modeling on mobile devices

Improvements on mobile devices allowed tracking applications to be executed on such platforms. However, there still remain several challenges in the field of mobile tracking, such as the extraction of high-level semantic information from point clouds. This task is more challenging when using monocular visual SLAM systems that output noisy sparse data. In this paper, we propose a primitive modeling method using both geometric and statistical analyses for sparse point clouds that can be executed on mobile devices. The main idea is to use the incremental mapping process of SLAM systems for analyzing the geometric relationship between the point cloud and the estimated shapes over time and selecting only reliably-modeled shapes. Besides that, a statistical evaluation that assesses if the modeling is random is incorporated to filter wrongly-detected primitives in unstable estimations. Our evaluation indicates that the proposed method was able to improve both precision and consistency of correct detection when compared with existing techniques. The mobile version execution is 8.5 to 9.9 times slower in comparison with the desktop implementation. However, it uses up to 30.5% of CPU load, which allows it to run on a separate thread, in parallel with the visual SLAM technique. Additional evaluations show that CPU load, energy consumption and RAM memory usage were not a concern when running our method on mobile devices.