Wildcat: Online Continuous-Time 3D Lidar-Inertial SLAM

TL;DR

Wildcat is an advanced online 3D lidar-inertial SLAM system that offers high robustness and versatility through continuous-time trajectory representation and efficient pose-graph optimization, excelling in challenging environments.

Contribution

It introduces a novel real-time lidar-inertial SLAM system combining continuous-time trajectories with pose-graph optimization for single- and multi-agent scenarios.

Findings

Outperformed other SLAM systems in DARPA Subterranean Challenge

Demonstrated superior robustness in diverse real-world environments

Showcased versatility across various sensing conditions

Abstract

We present Wildcat, a novel online 3D lidar-inertial SLAM system with exceptional versatility and robustness. At its core, Wildcat combines a robust real-time lidar-inertial odometry module, utilising a continuous-time trajectory representation, with an efficient pose-graph optimisation module that seamlessly supports both the single- and multi-agent settings. The robustness of Wildcat was recently demonstrated in the DARPA Subterranean Challenge where it outperformed other SLAM systems across various types of sensing-degraded and perceptually challenging environments. In this paper, we extensively evaluate Wildcat in a diverse set of new and publicly available real-world datasets and showcase its superior robustness and versatility over two existing state-of-the-art lidar-inertial SLAM systems.