MINS: Efficient and Robust Multisensor-aided Inertial Navigation System

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.

NeRF-VINS: A Real-time Neural Radiance Field Map-based Visual-Inertial Navigation System

Achieving accurate, efficient, and consistent localization within an a priori environment map remains a fundamental challenge in robotics and computer vision. Conventional map-based keyframe localization often suffers from sub-optimal viewpoints due to limited field of view (FOV), thus degrading its performance. To address this issue, in this paper, we design a real-time tightly-coupled Neural Radiance Fields (NeRF)-aided visual-inertial navigation system (VINS), termed NeRF-VINS. By effectively leveraging NeRF's potential to synthesize novel views, essential for addressing limited viewpoints, the proposed NeRF-VINS optimally fuses IMU and monocular image measurements along with synthetically rendered images within an efficient filter-based framework. This tightly coupled integration enables 3D motion tracking with bounded error. We extensively compare the proposed NeRF-VINS against the state-of-the-art methods that use prior map information, which is shown to achieve superior performance. We also demonstrate the proposed method is able to perform real-time estimation at 15 Hz, on a resource-constrained Jetson AGX Orin embedded platform with impressive accuracy.

LIC-Fusion: LiDAR-Inertial-Camera Odometry

This paper presents a tightly-coupled multi-sensor fusion algorithm termed LiDAR-inertial-camera fusion (LIC-Fusion), which efficiently fuses IMU measurements, sparse visual features, and extracted LiDAR points. In particular, the proposed LIC-Fusion performs online spatial and temporal sensor calibration between all three asynchronous sensors, in order to compensate for possible calibration variations. The key contribution is the optimal (up to linearization errors) multi-modal sensor fusion of detected and tracked sparse edge/surf feature points from LiDAR scans within an efficient MSCKF-based framework, alongside sparse visual feature observations and IMU readings. We perform extensive experiments in both indoor and outdoor environments, showing that the proposed LIC-Fusion outperforms the state-of-the-art visual-inertial odometry (VIO) and LiDAR odometry methods in terms of estimation accuracy and robustness to aggressive motions.