轻量级单目SLAM系统实现微小无人机实时3D稠密地图构建
Simultaneous Localization and 3D-Semi Dense Mapping for Micro Drones Using Monocular Camera and Inertial Sensors
- 结合关键点定位与边缘重建，提升地图几何精度
- 在DJI Tello上实现实时运行，定位误差低于10%
- 适合低功耗平台，适用于室内导航与避障
单目同步定位与建图（SLAM）算法利用单个摄像头估计无人机位姿并构建3D地图。现有方法中，稀疏方法缺乏细节几何信息，而学习驱动的稠密方法计算开销大，且单目SLAM存在尺度模糊问题。为此，我们提出一种面向边缘设备的轻量级单目SLAM系统，融合基于关键点的位姿估计与稠密边缘重建。该方法采用深度学习进行深度预测和边缘检测，并通过优化提升关键点与边缘的几何一致性，无需全局回环或重型神经网络计算。通过扩展卡尔曼滤波融合惯性数据，有效解决尺度模糊问题并提高精度。系统在搭载单目相机和惯性传感器的DJI Tello无人机上实现实时运行。在室内走廊及TUM RGBD数据集上验证了其在自主导航与障碍物避让中的鲁棒性。该方法为资源受限环境下的实时建图与导航提供了高效实用的解决方案。
原文摘要 · Abstract (English)
Monocular simultaneous localization and mapping (SLAM) algorithms estimate drone poses and build a 3D map using a single camera. Current algorithms include sparse methods that lack detailed geometry, while learning-driven approaches produce dense maps but are computationally intensive. Monocular SLAM also faces scale ambiguities, which affect its accuracy. To address these challenges, we propose an edge-aware lightweight monocular SLAM system combining sparse keypoint-based pose estimation with dense edge reconstruction. Our method employs deep learning-based depth prediction and edge detection, followed by optimization to refine keypoints and edges for geometric consistency, without relying on global loop closure or heavy neural computations. We fuse inertial data with vision by using an extended Kalman filter to resolve scale ambiguity and improve accuracy. The system operates in real time on low-power platforms, as demonstrated on a DJI Tello drone with a monocular camera and inertial sensors. In addition, we demonstrate robust autonomous navigation and obstacle avoidance in indoor corridors and on the TUM RGBD dataset. Our approach offers an effective, practical solution to real-time mapping and navigation in resource-constrained environments.
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