Periodic SLAM: Using Cyclic Constraints to Improve the Performance of Visual-Inertial SLAM on Legged Robots

Methods for state estimation that rely on visual information are challenging on legged robots due to rapid changes in the viewing angle of onboard cameras. In this work, we show that by leveraging structure in the way that the robot locomotes, the accuracy of visual-inertial SLAM in these challenging scenarios can be increased. We present a method that takes advantage of the underlying periodic predictability often present in the motion of legged robots to improve the performance of the feature tracking module within a visual-inertial SLAM system. Our method performs multi-session SLAM on a single robot, where each session is responsible for mapping during a distinct portion of the robot's gait cycle. Our method produces lower absolute trajectory error than several state-of-the-art methods for visual-inertial SLAM in both a simulated environment and on data collected on a quadrupedal robot executing dynamic gaits. On real-world bounding gaits, our median trajectory error was less than 35% of the error of the next best estimate provided by state-of-the-art methods.

I. Introduction

While there has been tremendous progress in the development of state estimation and simultaneous localization and mapping (SLAM) algorithms in recent years, dynamic motion can still induce failure on even the most robust systems [1]. More specifically, methods for state estimation and SLAM that rely on visual information experience a significant decrease in the performance of visual feature tracking when there are rapid changes in the viewing angle of cameras onboard a robot.