While LiDAR and cameras are becoming ubiquitous for unmanned aerial vehicles (UAVs) but can be ineffective in challenging environments, 4D millimeter-wave (MMW) radars that can provide robust 3D ranging and Doppler velocity measurements are less exploited for aerial navigation. In this paper, we develop an efficient and robust error-state Kalman filter (ESKF)-based radar-inertial navigation for UAVs. The key idea of the proposed approach is the point-to-distribution radar scan matching to provide motion constraints with proper uncertainty qualification, which are used to update the navigation states in a tightly coupled manner, along with the Doppler velocity measurements. Moreover, we propose a robust keyframe-based matching scheme against the prior map (if available) to bound the accumulated navigation errors and thus provide a radar-based global localization solution with high accuracy. Extensive real-world experimental validations have demonstrated that the proposed radar-aided inertial navigation outperforms state-of-the-art methods in both accuracy and robustness.

While Global Navigation Satellite System (GNSS) is often used to provide global positioning if available, its intermittency and/or inaccuracy calls for fusion with other sensors. In this paper, we develop a novel GNSS-Visual-Inertial Navigation System (GVINS) that fuses visual, inertial, and raw GNSS measurements within the square-root inverse sliding window filtering (SRI-SWF) framework in a tightly coupled fashion, which thus is termed SRI-GVINS. In particular, for the first time, we deeply fuse the GNSS pseudorange, Doppler shift, single-differenced pseudorange, and double-differenced carrier phase measurements, along with the visual-inertial measurements. Inherited from the SRI-SWF, the proposed SRI-GVINS gains significant numerical stability and computational efficiency over the start-of-the-art methods. Additionally, we propose to use a filter to sequentially initialize the reference frame transformation till converges, rather than collecting measurements for batch optimization. We also perform online calibration of GNSS-IMU extrinsic parameters to mitigate the possible extrinsic parameter degradation. The proposed SRI-GVINS is extensively evaluated on our own collected UAV datasets and the results demonstrate that the proposed method is able to suppress VIO drift in real-time and also show the effectiveness of online GNSS-IMU extrinsic calibration. The experimental validation on the public datasets further reveals that the proposed SRI-GVINS outperforms the state-of-the-art methods in terms of both accuracy and efficiency.

Accurate and real-time global pose estimation relative to a global prior map is indispensable in many applications, such as logistics with micro aerial vehicles and Augmented Reality. Supposed that a pure sparse 3D point map can provide a structureless representation of the environment, then generating a point-plane prior map can further model the environment topology and offer global constraints for an accurate localization. To implement this, we propose a filter-based, large-scale visual-inertial odometry system, termed PPM-VIO, which utilizes a point-plane map to correct the cumulative drift. Our system, detecting coplanar information from sparse point clouds with semantic information, achieves accurate online plane matching via geometric constraints, semantic constraints, and descriptor constraints. To improve the localization performance, we effectively integrate and formulate the global planar measurements and points measurements in a filter-based estimator. The effectiveness of the proposed method is extensively validated on real-world datasets collected in different scenarios. Experimental results demonstrate that, rather than using the point map alone, leveraging the plane information in the prior map can yield better trajectory estimates and broaden the effective scope of the prior map in different scenes.

This paper addresses the problem of visual-inertial odometry (VIO) with a downward facing monocular camera when a micro aerial vehicle (MAV) flying at high altitude (over 100 meters). It is important to note that large scene depth causes visual motion constraints significantly less informative than that in near-sighted scenarios as considered in most existing VIO methods. To cope with this challenge, we develop an efficient MSCKF-based VIO algorithm aided by a single 1D laser range finder (LRF), termed LRF-VIO, which runs in real time on an embedded system. The key idea of the proposed LRF-VIO is to fully exploit the limited metric distance information provided by the 1D LRF to disambiguate the scale during visual feature tracking, thus improving the VIO performance at high altitude. Specifically, during the MSCKF visual measurement update, we deliberately constrain the depth of those SLAM features co-planar with the single LRF measuring point. Additionally, delayed initialization of features utilizes the LRF measurements whenever possible, and online extrinsic calibration between the LRF and monocular camera is performed to further improve estimation accuracy and robustness. The proposed LRF-VIO is extensively validated in both indoor and outdoor real-world experiments, outperforming the state-of-the-art methods.