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.

Sensor fusion using fuzzy logic enhanced kalman filter for autonomous vehicle guidance in citrus groves

Abstract: This article discusses the development of a sensor fusion system for guiding an autonomous vehicle through citrus grove alleyways. The sensor system for path finding consists of machine vision and laser radar. An inertial measurement unit (IMU) is used for detecting the tilt of the vehicle, and a speed sensor is used to find the travel speed. A fuzzy logic enhanced Kalman filter was developed to fuse the information from machine vision, laser radar, IMU, and speed sensor. The fused information is used to guide a vehicle. The algorithm was simulated and then implemented on a tractor guidance system. The guidance system's ability to navigate the vehicle at the middle of the path was first tested in a test path. Average errors of 1.9 cm at 3.1 m s -1 and 1.5 cm at 1.8 m s -1 were observed in the tests. A comparison was made between guiding the vehicle using the sensors independently and using fusion. Guidance based on sensor fusion was found to be more accurate than guidance using independent sensors. The guidance system was then tested in citrus grove alleyways, and average errors of 7.6 cm at 3.1 m s -1 and 9.1 cm at 1.8 m s -1 were observed. Visually, the navigation in the citrus grove alleyway was as good

Evaluation of a solid-state gyroscope for robotics applications

Abstract: The evaluation of a low-cost solid-state gyroscope for robotics applications is described. An error model for the sensor is generated and included in a Kalman filter for estimating the orientation of a moving robot vehicle. Orientation estimation with the error model is compared to the performance when the error model is excluded from the system. The results demonstrate that without error compensation, the error in idealization is between 5-15/spl deg//min but can be improved at least by a factor of 5 if an adequate error model is supplied. Like all inertial systems, the platform requires additional information from some absolute position-sensing mechanism to overcome long-term drift. However, the results show that with careful and detailed modeling of error sources, inertial sensors can provide valuable orientation information for mobile robot applications. >

A Methodology for the Performance Evaluation of Inertial Measurement Units

Abstract: This paper presents a methodology for a reliable comparison among Inertial Measurement Units or attitude estimation devices in a Vicon environment. The misalignment among the reference systems and the lack of synchronization among the devices are the main problems for the correct performance evaluation using Vicon as reference measurement system. We propose a genetic algorithm coupled with Dynamic Time Warping (DTW) to solve these issues. To validate the efficacy of the methodology, a performance comparison is implemented between the WB-3 ultra-miniaturized Inertial Measurement Unit (IMU), developed by our group, with the commercial IMU InertiaCube3? by InterSense.

The Use of Augmented Reality, GPS and INS for Subsurface Data Visualisation

Abstract: Augmented Reality (AR) is a technology that allows information stored digitally to be overlaid graphically on views of the real world. A vast amount of such information currently resides in office-based computer systems but is not readily accessible to engineers and managers in the field. This paper addresses research being undertaken for AR systems that will allow people to look into the ground and see underground features. These features could be major geological structures, gas or water pipe-work or zones of contaminated land. This ability to view underground features will revolutionise many elements of fieldwork for a wide range of industries involved with the natural and built environment. Fundamental to the success of such a system is the ability to position the user with respect to the coordinate frame of the geographical database. Without position and orientation, overlaying the data for visualisation is impossible, if the solution is not accurate enough then registration errors will occur which will affect the usefulness of the system. The integration of kinematic GPS and INS allows centimetre level positioning and orientation to be achieved, opening up many applications using this tracker technology based in the field of personal navigation (Ladetto, 2000), (Judd, 1997). One such application researched at the University of Nottingham is the integration of this positioning technology with AR. The University of Nottingham is directly involved in developing what is known in the field of Augmented Reality as the Tracker Technology. The aim is to develop a modular approach to the solution enabling different grades of achievable accuracy and creating a technology demonstrator effective in a real environment. In terms of the required accuracy, it is envisaged that the highest quality of position and orientation will be achieved through using RTK GPS combined with an IMU utilising gyroscopes, accelerometers and magnetometers. The paper describes the research underway at the University of Nottingham concerning the integration of RTK GPS and an IMU to allow robust and precise real time positioning and orientation. This data is then used in the AR system to superimpose the virtual image onto the real-world view of the user. The basic concepts of AR are explained with emphasis on the tracking technology required for an effective system. Additionally some applications are discussed considering the different requirements of the positioning and visualisation system.

Automatic front-crawl temporal phase detection using adaptive filtering of inertial signals

Abstract: This study introduces a novel approach for automatic temporal phase detection and inter-arm coordination estimation in front-crawl swimming using inertial measurement units (IMUs). We examined the validity of our method by comparison against a video-based system. Three waterproofed IMUs (composed of 3D accelerometer, 3D gyroscope) were placed on both forearms and the sacrum of the swimmer. We used two underwater video cameras in side and frontal views as our reference system. Two independent operators performed the video analysis. To test our methodology, seven well-trained swimmers performed three 300 m trials in a 50 m indoor pool. Each trial was in a different coordination mode quantified by the index of coordination. We detected different phases of the arm stroke by employing orientation estimation techniques and a new adaptive change detection algorithm on inertial signals. The difference of 0.2 ± 3.9% between our estimation and video-based system in assessment of the index of coordination w...