Real-Time Bundle Adjustment for Ultra-High-Resolution UAV Imagery Using Adaptive Patch-Based Feature Tracking

Real-time processing of UAV imagery is crucial for applications requiring urgent geospatial information, such as disaster response, where rapid decision-making and accurate spatial data are essential. However, processing high-resolution imagery in real time presents significant challenges due to the computational demands of feature extraction, matching, and bundle adjustment (BA). Conventional BA methods either downsample images, sacrificing important details, or require extensive processing time, making them unsuitable for time-critical missions. To overcome these limitations, we propose a novel real-time BA framework that operates directly on fullresolution UAV imagery without downsampling. Our lightweight, onboard-compatible approach divides each image into user-defined patches (e.g., NxN grids, default 150x150 pixels) and dynamically tracks them across frames using UAV GNSS/IMU data and a coarse, globally available digital surface model (DSM). This ensures spatial consistency for robust feature extraction and matching between patches. Overlapping relationships between images are determined in real time using UAV navigation system, enabling the rapid selection of relevant neighbouring images for localized BA. By limiting optimization to a sliding cluster of overlapping images, including those from adjacent flight strips, the method achieves real-time performance while preserving the accuracy of global BA. The proposed algorithm is designed for seamless integration into the DLR Modular Aerial Camera System (MACS), supporting largearea mapping in real time for disaster response, infrastructure monitoring, and coastal protection. Validation on MACS datasets with 50MP images demonstrates that the method maintains precise camera orientations and high-fidelity mapping across multiple strips, running full bundle adjustment in under 2 seconds without GPU acceleration.

Channel-Aware Distillation Transformer for Depth Estimation on Nano Drones

Autonomous navigation of drones using computer vision has achieved promising performance. Nano-sized drones based on edge computing platforms are lightweight, flexible, and cheap, thus suitable for exploring narrow spaces. However, due to their extremely limited computing power and storage, vision algorithms designed for high-performance GPU platforms cannot be used for nano drones. To address this issue this paper presents a lightweight CNN depth estimation network deployed on nano drones for obstacle avoidance. Inspired by Knowledge Distillation (KD), a Channel-Aware Distillation Transformer (CADiT) is proposed to facilitate the small network to learn knowledge from a larger network. The proposed method is validated on the KITTI dataset and tested on a nano drone Crazyflie, with an ultra-low power microprocessor GAP8.

Lite-Mono: A Lightweight CNN and Transformer Architecture for Self-Supervised Monocular Depth Estimation

Self-supervised monocular depth estimation that does not require ground-truth for training has attracted attention in recent years. It is of high interest to design lightweight but effective models, so that they can be deployed on edge devices. Many existing architectures benefit from using heavier backbones at the expense of model sizes. In this paper we achieve comparable results with a lightweight architecture. Specifically, we investigate the efficient combination of CNNs and Transformers, and design a hybrid architecture Lite-Mono. A Consecutive Dilated Convolutions (CDC) module and a Local-Global Features Interaction (LGFI) module are proposed. The former is used to extract rich multi-scale local features, and the latter takes advantage of the self-attention mechanism to encode long-range global information into the features. Experiments demonstrate that our full model outperforms Monodepth2 by a large margin in accuracy, with about 80% fewer trainable parameters.