  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.
