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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.


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Patent
10 Feb 2005
TL;DR: In this article, a position tracking system for tracking the position of an object is disclosed, which includes a tracking device that is connected to or otherwise affixed to the object to be tracked.
Abstract: A position-tracking system for tracking the position of an object is disclosed. According to various embodiments, the tracking system includes a tracking device that is connected to or otherwise affixed to the object to be tracked. The tracking device may include, among other things, an inertial sensor assembly, radio transceivers and a processor. The position tracking system may also include a host processing system that is in communication with the tracking device. The position tracking system may provide variable-resolution position information based on the environment in which the object is moving. In a “wide resolution” area, the system may compute a general position for the object based on a wireless telephone network Cell-ID/map correlation architecture. In a high-resolution area, greater position resolution may be realized from the combination of a wireless aiding system and inputs from the inertial sensors. In the high-resolution mode, the system may exploit distinct patterns of motion that can be identified as motion “signatures” that are characteristic of certain types of motion. Kinematic (or object movement) models may be constructed based on these motion signatures and the position tracking system may estimate the state of the object based on the kinematic model for the current mode of the object. Adaptive and cascaded Kalman filtering may be employed in the analysis to more accurately estimate the position and velocity of the object based on the motion pattern identified.

70 citations

Proceedings ArticleDOI
01 Sep 2017
TL;DR: A six degrees of freedom LiDAR SLAM algorithm is redesigned to achieve 3D localization on the base map, as well as real-time vehicle navigation for unmanned ground vehicles.
Abstract: The ability to drive autonomously in heterogeneous environments without GPS, pattern identification (e.g. road following), or artificial landmarks is key to field robotics. To address this challenge, we present a complete waypoint navigation framework for unmanned ground vehicles. A Velodyne PUCK VLP-16 LiDAR and an IMU are mounted on an autonomous, full size utility vehicle and used for localization within a previously created base map. We redesign a six degrees of freedom LiDAR SLAM algorithm to achieve 3D localization on the base map, as well as real-time vehicle navigation. We fuse the low-frequency, high precision SLAM updates with high-frequency, odometric local state estimates from the vehicle. The navigation costmap consists of a 2D occupancy grid which is computed from the 3D base map. Relying on this setup, the vehicle is capable of navigating through a complex site completely autonomously. The test site has densely and sparsely built areas, bushland, industrial activities, pedestrians, and other manned or unmanned vehicles. Extensive testing was done using both current and outdated base maps for comparisons, and a high precision RTK-GPS was used for ground truth. So far, more than 60 km of completely autonomous driving has been performed without a single system or navigation failure.

70 citations

Journal ArticleDOI
TL;DR: Two novel approaches to estimate accurately mobile robot attitudes based on the fusion of low-cost accelerometers and gyroscopes are proposed and a novel adaptive Kalman filter structure is introduced that modifies the noise covariance values according to the system dynamics.

70 citations

Journal ArticleDOI
Lu Xiong1, Xin Xia1, Lu Yishi1, Wei Liu1, Gao Letian1, Song Shunhui1, Zhuoping Yu1 
TL;DR: An inertial measurement unit (IMU)-based automated vehicle body sideslip angle and attitude estimation method aided by low-sample-rate global navigation satellite system (GNSS) velocity and position measurements using parallel adaptive Kalman filters is proposed.
Abstract: The sideslip angle and attitude are crucial for automated driving especially for chassis integrated control and environmental perception. In this article an inertial measurement unit (IMU)-based automated vehicle body sideslip angle and attitude estimation method aided by low-sample-rate global navigation satellite system (GNSS) velocity and position measurements using parallel adaptive Kalman filters is proposed. This method can estimate the sideslip angle and attitude simultaneously and is robust against the vehicle parameters and road friction even as the vehicle enters critical maneuvers. First, based on the acceleration and angular rate from the six-dimensional inertial measurement unit, the attitude, velocity and position (AVP) are integrated with the navigation coordinates and the AVP error dynamics and observation equations of the integration results are developed. Second, parallel innovation adaptive estimation (IAE)-based Kalman filters is designed to estimate the AVP error of the integration method to address the issues of the GNSS low sampled rate and abnormal measurements. Then the AVP error is forwarded to the AVP integration to compensate the accumulated error. To improve the heading angle estimation accuracy, the heading error is estimated by a decoupled IAE-based Kalman filter aided by GNSS heading. In addition, time synchronization of the IMU and GNSS is realized through hardware based on the pulse per second signal of the GNSS receiver and the spatial synchronization is achieved by a direct compensation method. Lastly, the sideslip angle and attitude estimation method is validated by a comprehensive experimental test including critical double lane change and slalom maneuvers. The results show that the estimation error of the longitudinal velocity and lateral velocity is smaller than 0.1 m/s $({1\sigma })$ , and the estimation error of the sideslip angle is smaller than 0.15° $({1\sigma })$ .

70 citations

Journal ArticleDOI
TL;DR: This paper aims to enhance long-term performance of conventional SINS/GPS navigation systems using a fuzzy adaptive integration scheme using a knowledge-based fuzzy inference system for decision-making between the AHRS and the SINS according to vehicle maneuvering conditions.

70 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20231,067
20222,256
2021852
20201,150
20191,181
20181,162