说明：  一篇讲解imu+slam的文章，在本论文中，我们研究了在大型初始未知环境中导航微型直升机时出现的车 辆状态估计和传感器自校准问题。虽然适当的车辆状态估计允许长期导航， 但是传感器自校准使得车辆成为电源随时随地的系统而无需先前和离线校准 步骤。由于空中飞行器固有的有效载荷限制，特别是微型直升机以及室内或 城市环境中全球定位信息的限制，我们专注于基于视觉的方法以实现我们的 目标。尽管我们专注于处理视觉线索，但我们还是分析了一种通用的模块化 方法，它允许使用各种不同的传感器类型
(In this paper, we study the problem of vehicle state estimation and sensor self-calibration when navigating micro helicopters in large initial unknown environments. Although proper vehicle state estimation allows long-term navigation, sensor self-calibration makes the vehicle a power supply system anywhere and anytime without prior and offline calibration steps. Due to the inherent payload limitations of air vehicles, especially the limitations of micro helicopters and global positioning information in indoor or urban environments, we focus on visual-based methods to achieve our goals. Despite our focus on processing visual cues, we analyzed a generic modular approach that allows the use of a variety of sensor types.)