Inertial measurement unit

About: Inertial measurement unit is a research topic. Over the lifetime, 13326 publications have been published within this topic receiving 189083 citations. The topic is also known as: IMU.

LIDAR-inertial integration for UAV localization and mapping in complex environments

Abstract: This paper presents customized techniques for autonomous localization and mapping of micro Unmanned Aerial Vehicles flying in complex environments, e.g. unexplored, full of obstacles, GPS challenging or denied. The proposed algorithms are aimed at 2D environments and are based on the integration of 3D data, i.e. point clouds acquired by means of a laser scanner (LIDAR), and inertial data given by a low cost Inertial Measurement Unit (IMU). Specifically, localization is performed by exploiting a scan matching approach based on a customized version of the Iterative Closest Point algorithm, while mapping is done by extracting robust line features from LIDAR measurements. A peculiarity of the line detection method is the use of the Principal Component Analysis which allows computational time saving with respect to traditional least squares techniques for line fitting. Performance of the proposed approaches is evaluated on real data acquired in indoor environments by means of an experimental setup including an UTM-30LX-EW 2D LIDAR, a Pixhawk IMU, and a Nitrogen board.

Online Serial Manipulator Calibration Based on Multisensory Process Via Extended Kalman and Particle Filters

Abstract: An online robot self-calibration method based on an inertial measurement unit (IMU) and a position sensor is presented in this paper. In this method, a position marker and an IMU are required to be rigidly attached to the robot tool to obtain the position of the manipulator from the position sensor and the orientation of the manipulator from the IMU in real time. An efficient approach that incorporates a Kalman filter (KF) and a particle filter to estimate the position and orientation of the manipulator is proposed in this paper. The use of these pose (orientation and position) estimation methods improves the reliability and accuracy of pose measurements. Finally, an extended KF is used to estimate the kinematic parameter errors. The primary advantage of this method over existing automated self-calibration methods is that it does not involve complex steps, such as camera calibration, corner detection, and laser alignment, which makes the proposed robot calibration procedure more autonomous in a dynamic manufacturing environment. Moreover, the reduction of complex steps improves the accuracy of calibration. Experimental studies on a GOOGOL GRB3016 robot show that the proposed method has better accuracy, convenience, and effectiveness.

A Multi-Sensorial Simultaneous Localization and Mapping (SLAM) System for Low-Cost Micro Aerial Vehicles in GPS-Denied Environments

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 configuration 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 configuration 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.

A Novel Method to Integrate IMU and Magnetometers in Attitude and Heading Reference Systems

Abstract: Modern attitude and heading reference systems (AHRS) generally use Kalman filters to integrate gyros with some other augmenting sensors, such as accelerometers and magnetometers, to provide a long term stable orientation solution. The construction of the Kalman filter for the AHRS is flexible, while the general options are the methods based on quaternion, Euler angles, or Euler angle errors. But the quaternion and Euler angle based methods need to model system angular motions, and, meanwhile, all these three methods suffer from nonlinear problems which will increase the system complexities and the computational difficulties. This paper proposes a novel implementation method for the AHRS integrating IMU and magnetometer sensors. In the proposed method, the Kalman filtering is implemented to use the Euler angle errors to express the local level frame (l frame) errors, rather than express the body frame (b frame) errors as the customary methods do. A linear system error model based on the Euler angles errors expressing the l frame errors for the AHRS has been developed and the corresponding system observation model has been derived. This proposed method for AHRS does not need to model system angular motions and also avoids the nonlinear problem which is inherent in the commonly used methods. The experimental results show that the proposed method is a promising alternative for the AHRS.

Micromachined Inertial Sensors: The State-of-the-Art and a Look into the Future:

Abstract: In the paper a review is given of the current state of the art of micromachined inertial sensors accelerometers and gyroscopes. These sensors can be used in a wide range of applications and micromachined devices have a number of significant advantages over their conventional counterparts such as lower cost, smaller form factor and lower power consumption. An overview will be given over the diverse technical implementations which can be classified by the manufacturing process, the type of transduction mechanism, and the type of control system. While micromachined accelerometers are already commercially available from a range of companies, gyroscopes are subject to intensive research worldwide and a series of problems remains to be resolved. A novel approach to inertial sensing, currently under investigation at Southampton University, is introduced which relies on electrostatic levitation and has the potential to overcome some inherent drawbacks of prevailing concepts.