Closed-Loop Benchmarking of Stereo Visual-Inertial SLAM Systems: Understanding the Impact of Drift and Latency on Tracking Accuracy

TL;DR

This paper evaluates how drift and latency in visual-inertial SLAM systems affect robot trajectory tracking in closed-loop navigation, highlighting the importance of reducing latency for improved accuracy.

Contribution

It introduces a closed-loop benchmarking framework to analyze the impact of latency and drift on SLAM performance, providing new insights into system improvements.

Findings

Latency significantly affects tracking accuracy

Reducing visual estimation latency improves trajectory following

Benchmarking reveals key areas for SLAM system enhancement

Abstract

Visual-inertial SLAM is essential for robot navigation in GPS-denied environments, e.g. indoor, underground. Conventionally, the performance of visual-inertial SLAM is evaluated with open-loop analysis, with a focus on the drift level of SLAM systems. In this paper, we raise the question on the importance of visual estimation latency in closed-loop navigation tasks, such as accurate trajectory tracking. To understand the impact of both drift and latency on visual-inertial SLAM systems, a closed-loop benchmarking simulation is conducted, where a robot is commanded to follow a desired trajectory using the feedback from visual-inertial estimation. By extensively evaluating the trajectory tracking performance of representative state-of-the-art visual-inertial SLAM systems, we reveal the importance of latency reduction in visual estimation module of these systems. The findings suggest directions of future improvements for visual-inertial SLAM.