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A Multi-Sensorial Simultaneous Localization and Mapping (SLAM) System for Low-Cost Micro Aerial Vehi

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【A Multi-Sensorial Simultaneous Localization and Mapping (SLAM) System for Low-Cost Micro Aerial Vehi】怀抱观古今，寝食展戏谑。这篇文章主要讲述A Multi-Sensorial Simultaneous Localization and Mapping (SLAM) System for Low-Cost Micro Aerial Vehi相关的知识，希望能为你提供帮助。
A Multi-Sensorial Simultaneous Localization and Mapping (SLAM) System for Low-Cost Micro Aerial Vehicles in GPS-Denied Environments
一种在无GPS环境中设计的面向低价微型飞行器的多传感器同步定位成图系统
学术编辑：Gonzalo Pajares Martinsanz
收到：2017年1月25日；接受：2017年4月5日；发布时间：4月8日201
Abstract: One of the main challenges of aerial robots navigation in indoor or GPS-denied environments is position estimation using only the available onboard sensors. This paper presents a Simultaneous Localization and Mapping (SLAM) system that remotely calculates the pose and environment map of different low-cost commercial aerial platforms, whose onboard computing capacity is usually limited. The proposed system adapts to the sensory con?guration of the aerial robot, by integrating different state-of-the art SLAM methods based on vision, laser and/or inertial measurements using an Extended Kalman Filter (EKF). To do this, a minimum onboard sensory con?guration is supposed, consisting of a monocular camera, an Inertial Measurement Unit (IMU) and an altimeter. It allows to improve the results of well-known monocular visual SLAM methods (LSD-SLAM and ORB-SLAM are tested and compared in this work) by solving scale ambiguity and providing additional information to the EKF.When payload and computational capabilities permit, a 2D laser sensor can be easily incorporated to the SLAM system, obtaining a local 2.5D map and a footprint estimation of the robot position that improves the 6D pose estimation through the EKF. We present some experimental results with two different commercial platforms, and validate the system by applying it to their position control.
简介：空中机器人在无GPS信号的环境中的一个主要挑战是只使用可用的机载传感器的位置估计。本文提出了一种同时建图和定位（SLAM）系统，它可以远程计算不同低价格商用航空平台的位姿和环境地图，远程计算的原因是机载计算能力通常是受限的。该系统适合于空中机器人的传感器配置，通过融合不同的先进的SLAM方法，包括视觉SLAM，激光雷达SLAM和/或者使用EKF的惯性测量。要做到这一点，一种最小的机载传感配置是可以做到的，包括一个单目相机、一个惯性测量单位（IMU）和一个高度计。

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