VIP-SLAM: An Efficient Tightly-Coupled RGB-D Visual Inertial Planar SLAM

TL;DR

VIP-SLAM introduces a lightweight, tightly-coupled RGB-D visual inertial SLAM system that leverages planar structures to significantly reduce computational complexity while maintaining high accuracy, suitable for mobile devices.

Contribution

The paper presents a novel SLAM system that integrates plane information to simplify the map and accelerate bundle adjustment without sacrificing accuracy.

Findings

Global bundle adjustment is nearly 2 times faster.

The system achieves better accuracy and efficiency compared to traditional sparse point-based SLAM.

Effective use of plane structures reduces computational complexity in indoor environments.

Abstract

In this paper, we propose a tightly-coupled SLAM system fused with RGB, Depth, IMU and structured plane information. Traditional sparse points based SLAM systems always maintain a mass of map points to model the environment. Huge number of map points bring us a high computational complexity, making it difficult to be deployed on mobile devices. On the other hand, planes are common structures in man-made environment especially in indoor environments. We usually can use a small number of planes to represent a large scene. So the main purpose of this article is to decrease the high complexity of sparse points based SLAM. We build a lightweight back-end map which consists of a few planes and map points to achieve efficient bundle adjustment (BA) with an equal or better accuracy. We use homography constraints to eliminate the parameters of numerous plane points in the optimization and reduce the complexity of BA. We separate the parameters and measurements in homography and point-to-plane constraints and compress the measurements part to further effectively improve the speed of BA. We also integrate the plane information into the whole system to realize robust planar feature extraction, data association, and global consistent planar reconstruction. Finally, we perform an ablation study and compare our method with similar methods in simulation and real environment data. Our system achieves obvious advantages in accuracy and efficiency. Even if the plane parameters are involved in the optimization, we effectively simplify the back-end map by using planar structures. The global bundle adjustment is nearly 2 times faster than the sparse points based SLAM algorithm.