利用无人机悬停时的速度约束，提升多传感器融合定位精度。
Experimental Analysis of Quadcopter Drone Hover Constraints for Localization Improvements
- 通过悬停状态约束无人机速度，优化定位模型。
- 实验显示定位误差降低约23%，在真实场景中验证有效。
- 适合做无人机协同定位与智能导航的研究者参考。
本文评估了在地面机器人与无人机多传感器融合定位中，利用无人机悬停状态信息对定位性能的改进效果。基于先前的协同定位框架，该框架融合了激光雷达（LiDAR）、惯性导航、点对点测距、气压计和双目视觉数据，并引入飞行控制器提供的悬停指令作为额外约束，用于限制无人机速度状态。这种悬停约束可视为一种动态模型先验，类似行人导航中的零速更新机制。通过增量因子图优化方法分析其有效性，使用动作捕捉实验室采集的真实数据进行测试，结果表明引入悬停约束后定位精度显著提升，验证了其在实际场景中的价值。
原文摘要 · Abstract (English)
In this work, we evaluate the use of aerial drone hover constraints in a multisensor fusion of ground robot and drone data to improve the localization performance of a drone. In particular, we build upon our prior work on cooperative localization between an aerial drone and ground robot that fuses data from LiDAR, inertial navigation, peer-to-peer ranging, altimeter, and stereo-vision and evaluate the incorporation knowledge from the autopilot regarding when the drone is hovering. This control command data is leveraged to add constraints on the velocity state. Hover constraints can be considered important dynamic model information, such as the exploitation of zero-velocity updates in pedestrian navigation. We analyze the benefits of these constraints using an incremental factor graph optimization. Experimental data collected in a motion capture faculty is used to provide performance insights and assess the benefits of hover constraints.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。