The goal of this paper is to create a new framework for dense SLAM that is
light enough for micro-robot systems based on depth camera and inertial sensor.
Feature-based and direct methods are two mainstreams in visual SLAM. Both
methods minimize photometric or reprojection error by iterative solutions,
which are computationally expensive. To overcome this problem, we propose a
non-iterative framework to reduce computational requirement. First, the
attitude and heading reference system (AHRS) and axonometric projection are
utilized to decouple the 6 Degree-of-Freedom (DoF) data, so that point clouds
can be matched in independent spaces respectively. Second, based on single
key-frame training, the matching process is carried out in frequency domain by
Fourier transformation, which provides a closed-form non-iterative solution. In
this manner, the time complexity is reduced to $\mathcal{O}(n \log{n})$, where
$n$ is the number of matched points in each frame. To the best of our
knowledge, this method is the first non-iterative and online trainable approach
for data association in visual SLAM. Compared with the state-of-the-arts, it
runs at a faster speed and obtains 3-D maps with higher resolution yet still
with comparable accuracy.

本论文提出了一种基于深度相机和惯性传感器的微型机器人系统的新型密集 SLAM 的框架，使用非迭代的 Fourier 变换来减少计算需求，通过解耦 6 自由度数据和进行独立空间点云匹配，实现分类键帧训练以及数据关联，相较现有方法，其速度更快，分辨率更高，但精度相当。