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.

Pedestrian Simultaneous Localization and Mapping in Multistory Buildings Using Inertial Sensors

Abstract: Pedestrian navigation is an important ingredient for efficient multimodal transportation, such as guidance within large transportation infrastructures. A requirement is accurate positioning of people in indoor multistory environments. To achieve this, maps of the environment play a very important role. Foot-SLAM is an algorithm based on the simultaneous localization and mapping (SLAM) principle that relies on human odometry, i.e., measurements of a pedestrian’s steps, to build probabilistic maps of human motion for such environments and can be applied using crowdsourcing. In this paper, we extend FootSLAM to multistory buildings following a Bayesian derivation. Our approach employs a particle filter and partitions the map space into a grid of adjacent hexagonal prisms with eight faces. We model the vertical component of the odometry errors using an autoregressive integrated moving average (ARIMA) model and extend the geographic tree-based data structure that efficiently stores the probabilistic map, allowing real-time processing. We present the multistory FootSLAM maps that were created from three data sets collected in different buildings (one large office building and two university buildings). Hereby, the user was only carrying a single foot-mounted inertial measurement unit (IMU). We believe the resulting maps to be strong evidence of the robustness of FootSLAM. This paper raises the future possibility of crowdsourced indoor mapping and accurate navigation using other forms of human odometry, e.g., obtained with the low-cost and nonintrusive sensors of a handheld smartphone.

Improving the Accuracy of Human Body Orientation Estimation With Wearable IMU Sensors

Abstract: Accurately estimating the orientation of different human body segments using low cost inertial sensors is a key component in various activity-related and healthcare-related applications. Typically, the signals from a gyroscope and an accelerometer are fused inside a Kalman filter to determine the orientation. However, the accelerometer measurements are influenced by the linear accelerations of the body segments in addition to the gravitational acceleration that corrupts the orientation estimates. The conventional method to deal with linear acceleration is to model it as a first-order low-pass process and estimate it inside the Kalman filter. In this conventional method, important information from those sensor axes that do not experience linear accelerations is lost. In this paper, we modify the conventional approach to deal with the problem of linear acceleration more efficiently. The proposed approach estimates the direction of linear acceleration and assigns lower weights inside the Kalman filter to only those sensor axes that are experiencing acceleration, thus conserving important information from other axes measurements. The proposed method is compared with the conventional method using simulations and experimentation on a test subject performing daily routine tasks. The results indicate a significant performance improvement in orientation estimation.

It's the human that matters: accurate user orientation estimation for mobile computing applications

Abstract: Ubiquity of Internet-connected and sensor-equipped portable devices sparked a new set of mobile computing applications that leverage the proliferating sensing capabilities of smartphones. For many of these applications, accurate estimation of the user heading, as compared to the phone heading, is of paramount importance. This is of special importance for many crowd-sensing applications, where the phone can be carried in arbitrary positions and orientations relative to the user body. Current state-of-the-art focus mainly on estimating the phone orientation, require the phone to be placed in a particular position, require user intervention, and/or do not work accurately indoors; which limits their ubiquitous usability in different applications. In this paper we present Humaine, a novel system to reliably and accurately estimate the user orientation relative to the Earth coordinate system. Humaine requires no prior-configuration nor user intervention and works accurately indoors and outdoors for arbitrary cell phone positions and orientations relative to the user body. The system applies statistical analysis techniques to the inertial sensors widely available on today's cell phones to estimate both the phone and user orientation. Implementation of the system on different Android devices with 170 experiments performed at different indoor and outdoor testbeds shows that Humaine significantly outperforms the state-of-the-art in diverse scenarios, achieving a median accuracy of 15° averaged over a wide variety of phone positions. This is 558% better than the-state-of-the-art. The accuracy is bounded by the error in the inertial sensors readings and can be enhanced with more accurate sensors and sensor fusion.

Robust foot clearance estimation based on the integration of foot-mounted IMU acceleration data

Abstract: This paper introduces a method for the robust estimation of foot clearance during walking , using a single inertial measurement unit (IMU) placed on the subject ' s foot. The proposed solution is based on double integration and drift cancellation of foot acceleration signals. The method is insensitive to misalignment of IMU axes with respect to foot axes. Details are provided regarding calibration and signal processing procedures. Experimental validation was performed on 10 healthy subjects under three walking conditions : normal , fast and with obstacles. Foot clearance estimation results were compared to measurements from an optical motion capture system. The mean error between them is significantly less than 15% under the various walking conditions.

Error analysis and stochastic modeling of MEMS based inertial sensors for land vehicle navigation applications

Abstract: Although GPS measurements are the essential information for currently developed land vehicle navigation systems (LVNS), the situation when GPS signals are unavailable or unreliable due to signal blockages must be compensated to provide continuous navigation solutions. In order to overcome the unavailability or unreliability problem in satellite based navigation systems and also to be cost effective, Micro Electro Mechanical Systems (MEMS) based inertial sensor technology has pushed the development of lowcost integrated navigation systems for land vehicle navigation and guidance applications. In spite of low inherent cost, small size, low power consumption, and solid reliability of MEMS based inertial sensors, the errors in the observations from the MEMS-based sensors must be appropriately treated in order to turn the observations into useful data for vehicle position determination. The error analysis would be conducted in the time domain specifying the stochastic variation as well as error sources of systematic nature. This thesis will address the above issues and present algorithms to identify and model the error sources in MEMS-based inertial sensors. A Kalman filter will be described and applied to analyze the performance of a minimum configured GPS/IMU system for vehicle navigation applications. The performance of the testing system has been assessed via a comparison to Precise Point Position (PPP) reference data. The testing results indicate the effectiveness of the discussed error analysis and modeling method. iii