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.

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.