The recently developed Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) have shown encour-aging and impressive results for visual SLAM. However, most representative methods require RGBD sensors and are only available for indoor environments. The robustness of reconstruction in largescale outdoor scenarios remains unexplored. This paper introduces a large-scale 3DGS-based visual SLAM with stereo cameras, termed LSG-SLAM. The proposed LSG-SLAM employs a multi-modality strategy to estimate prior poses under large view changes. In tracking, we introduce feature-alignment warping constraints to alleviate the adverse effects of appearance similarity in rendering losses. For the scalability of large-scale scenarios, we introduce continuous Gaussian Splatting submaps to tackle unbounded scenes with limited memory. Loops are detected between GS sub maps by place recognition and the relative pose between looped keyframes is optimized utilizing rendering and feature warping losses. After the global optimization of camera poses and Gaussian points, a structure refinement module enhances the reconstruction quality. With extensive evaluations on the EuRoc and KITTI datasets, LSG-SLAM achieves superior performance over existing Neural, 3DGS-based, and even traditional approaches. Project page: https://lsg-slam.github.io.

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.

UAV-view geo-localization is crucial in many applications, such as material transportation and security inspection, particularly in GPS-denied urban environments. However, most existing methods assume a known drone flight altitude and divide satellite maps into tiles that approximate the scale of drone images, which are often inapplicable to real-world UAV scenarios where flight altitudes vary. In this paper, we propose a novel UAV-view geo-localization method, termed MM-Geo, to address the aforementioned issue. In particular, we partition the satellite imagery map into tiles of uniform size and retrieve the matching tiles in real time using online drone images of smaller field-of-view (FOV) at different altitudes. To address the multi-scale problem due to the varying altitudes, we design the patch vote rerank with match attention, and to tackle the multi-positive sample issue in the continuous, the normalized infoNCE loss is incorporated to provide finer supervision during contrastive learning. The proposed MM-Geo is extensively validated on the our own large-scale urban dataset MT-UAV as well as the public datasets UAV-VisLoc, outperforming the state-of-the-art (SOTA) approaches and achieving remarkable performance in practical drone delivery operations. To benefit the community, we will release the VisLoc-related code at: https://github.com/MM-Geo-2025/MM-Geo.

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.

In recent years, multirotors have become more and more widely used, such as in aerial photography and delivery. Ensuring a safe landing in emergencies is the most basic requirement, and it is important to make full use of all the sensors of the multirotor. To improve the safety of UAV landing in unknown unstructured scenes, this paper proposes a multi-FOV-constrained trajectory planning algorithm. Due to the discontinuity of multi-FOV constraints and the nonlinearity of UAV dynamics, the entire trajectory planning problem is a nonlinear optimization problem with non-convex constraints. To address this problem, our algorithm contains two stages, a multi-fov-constrained path search algorithm and a safe landing trajectory optimization algorithm. The multi-fov-constrained path search algorithm is used to generate a safe initial path that satisfies the FOV constraint. Then, the safe landing trajectory optimization algorithm generates a safe trajectory, which considers FOV constraints, dynamics, smoothness, and obstacle avoidance. We conducted simulation experiments and real-world experiments to verify the robustness and effectiveness of our algorithm.

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.

Bundle adjustment (BA) is a well-studied fundamental problem in the robotics and vision community. In man-made environments, coplanar points and lines are ubiquitous. However, the number of works on bundle adjustment with coplanar points and lines is relatively small. This paper focuses on this special BA problem, referred to as$\pi-\mathbf{BA}$. For a point or a line on a plane, we derive a new constraint to describe the relationship among two poses and the plane, called$\pi$-constraint. We distribute$\pi$-constraints into different groups. Each group is called a$\pi$-factor. We prove that, with some simple preprocessing, the computational complexity associated with a$\pi$-factor in the Levenberg-Marquardt (LM) algorithm is$O(1)$, independent of the number of$\pi$-constraints packed into the$\pi$-factor. In$\pi-\mathbf{BA}, \pi$-factors replace original reprojection errors. One problem is how to divide$\pi$-constraints into$\pi$-factors. Different strategies may result in different numbers of$\pi$-factors, which in turn affects the efficiency. It is difficult to get the optimal division. We present a greedy algorithm to overcome this problem. Experimental results verify that our algorithm can significantly accelerate the computation.

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.

This paper introduces an efficient direct visual odometry (VO) algorithm using points and lines. Pixels on lines are generally adopted in direct methods. However, the original photometric error is only defined for points. It seems difficult to extend it to lines. In previous works, the collinear constraints for points on lines are either ignored [1] or introduce heavy computational load into the resulting optimization system [2]. This paper extends the photometric error for lines. We prove that the 3D points of the points on a 2D line are determined by the inverse depths of the endpoints of the 2D line, and derive a closed-form solution for this problem. This property can significantly reduce the number of variables to speed up the optimization, and can make the collinear constraint exactly satisfied. Furthermore, we introduce a two-step method to further accelerate the optimization, and prove the convergence of this method. The experimental results show that our algorithm outperforms the state-of-the-art direct VO algorithms.