Robust multisensor fusion of multi-modal measurements such as IMUs, wheel encoders, cameras, LiDARs, and GPS holds great potential due to its innate ability to improve resilience to sensor failures and measurement outliers, thereby enabling robust autonomy. To the best of our knowledge, this work is among the first to develop a consistent tightly-coupled Multisensor-aided Inertial Navigation System (MINS) that is capable of fusing the most common navigation sensors in an efficient filtering framework, by addressing the particular challenges of computational complexity, sensor asynchronicity, and intra-sensor calibration. In particular, we propose a consistent high-order on-manifold interpolation scheme to enable efficient asynchronous sensor fusion and state management strategy (i.e. dynamic cloning). The proposed dynamic cloning leverages motion-induced information to adaptively select interpolation orders to control computational complexity while minimizing trajectory representation errors. We perform online intrinsic and extrinsic (spatiotemporal) calibration of all onboard sensors to compensate for poor prior calibration and/or degraded calibration varying over time. Additionally, we develop an initialization method with only proprioceptive measurements of IMU and wheel encoders, instead of exteroceptive sensors, which is shown to be less affected by the environment and more robust in highly dynamic scenarios. We extensively validate the proposed MINS in simulations and large-scale challenging real-world datasets, outperforming the existing state-of-the-art methods, in terms of localization accuracy, consistency, and computation efficiency. We have also open-sourced our algorithm, simulator, and evaluation toolbox for the benefit of the community: https://github.com/rpng/mins.

翻译：多模态测量（如IMU、轮式编码器、摄像头、LiDAR和GPS）的鲁棒多传感器融合，因其天然具备提升对传感器故障和测量异常值的适应能力，从而能够实现鲁棒自主性，因此具有巨大潜力。据我们所知，本文是首批开发出一致性紧耦合多传感器辅助惯性导航系统（MINS）的工作之一，该系统通过解决计算复杂度、传感器异步性及传感器内部标定的特定挑战，能够在高效滤波框架中融合最常见的导航传感器。具体而言，我们提出了一种一致的高阶流形插值方案，以实现高效的异步传感器融合和状态管理策略（即动态克隆）。所提出的动态克隆利用运动信息来自适应选择插值阶数，从而在最小化轨迹表示误差的同时控制计算复杂度。我们对所有机载传感器进行在线内参及外参（时空）标定，以补偿不准确的先验标定和/或随时间退化的标定。此外，我们仅利用IMU和轮式编码器的本体感知测量值（而非外部感知传感器）开发了一种初始化方法，该方法受环境影响较小，在高动态场景中更具鲁棒性。我们在仿真实验和大规模具有挑战性的真实世界数据集上对提出的MINS进行了广泛验证，结果表明其在定位精度、一致性和计算效率方面均优于现有最先进方法。我们已为回馈社区而开源了算法、仿真器及评估工具箱：https://github.com/rpng/mins。