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In this paper, we propose a tightly-coupled, multi-modal simultaneouslocalization and mapping (SLAM) framework, integrating an extensive set ofsensors: IMU, cameras, multiple lidars, and Ultra-wideband (UWB) rangemeasurements, hence referred to as VIRAL (visual-inertial-ranging-lidar) SLAM.To achieve such a comprehensive sensor fusion system, one has to tackle severalchallenges such as data synchronization, multi-threading programming, bundleadjustment (BA), and conflicting coordinate frames between UWB and the onboardsensors, so as to ensure real-time localization and smooth updates in the stateestimates. To this end, we propose a two stage approach. In the first stage,lidar, camera, and IMU data on a local sliding window are processed in a coreodometry thread. From this local graph, new key frames are evaluated foradmission to a global map. Visual feature-based loop closure is also performedto supplement the global factor graph with loop constraints. When the globalfactor graph satisfies a condition on spatial diversity, the BA process will betriggered to update the coordinate transform between UWB and onboard SLAMsystems. The system then seamlessly transitions to the second stage where allsensors are tightly integrated in the odometry thread. The capability of oursystem is demonstrated via several experiments on high-fidelitygraphical-physical simulation and public datasets.

VIRAL SLAM: Tightly Coupled Camera-IMU-UWB-Lidar SLAM