Context-Aided Inertial Navigation viaBelief Condensation

Abstract

Inertial navigation systems suffer from drift errors that degrade their performance. Main current techniques mitigate such errors by detecting stance phases under the specific context of pedestrian walking with a foot-mounted inertial measurement unit (IMU). Existing approaches achieve acceptable performances only in simple circumstances, such as smooth movements and short periods of time. In addition, they lack a principled unifying methodology to exploit contextual information. In this paper, we establish a general framework for context-aided inertial navigation, and present efficient algorithms for its implementation based on the inference technique called belief condensation (BC). The performance of the proposed techniques is evaluated against the state of the art through an experimental case study. Our results show that the proposed techniques can remarkably improve the navigation accuracy while keeping moderate complexities.

The Error Propagation Analysis of the Received Signal Strength-Based Simultaneous Localization and Tracking in Wireless Sensor Networks

“…Recently, a general Inertial navigation framework was proposed in [64] to exploit contextual information, which improves navigation performance. In applications, the sampling and reconstruction theory of a finite-energy signal with uncertainties in [65] can be utilized to devise practical mobile target tracking algorithms with reference node location errors.…”

Particle filter for context sensitive indoor pedestrian navigation

“…Prieto discusses in [2] on how to detect gait phases effectively using belief condensation. Recorded velocity, acceleration and angular velocity values are divided into clusters of stance and swing phases.…”

Foot-Mounted Inertial Measurement Units-Based Device for Ankle Rehabilitation

“…Inertial measurement units (IMU) can be used to make portable rehabilitation devices. They have been extensively used in position and attitude estimation, allowing for pedestrian navigation tracking [6][7][8][9][10][11][12][13], gait analysis [14][15][16][17][18][19][20], foot pose estimation [21], foot clearance estimation [22], wearable devices for Game Play application [23], and detecting foot strike in recreational runners [24]. A problem of inertial measurement units is that they present biases and other systematic errors that are responsible for position and attitude estimation errors.…”

“…To provide robust distortion-free and refined absolute position and orientation vectors, data fusion is used to combine the measurements from the three-axis gyroscope, three-axis geomagnetic sensor, and three-axis accelerometer enclosed in the IMU [17]. Approaches used with data fusion include Kalman filters [6,9,15], clustering algorithms [7], and hidden Markov models [8]. Some of these approaches rely on additional information, such as map information [8,10], multiple IMU [11], biomechanical models [25], or light detection and ranging (LIDAR) systems [12].…”

“…Approaches used with data fusion include Kalman filters [6,9,15], clustering algorithms [7], and hidden Markov models [8]. Some of these approaches rely on additional information, such as map information [8,10], multiple IMU [11], biomechanical models [25], or light detection and ranging (LIDAR) systems [12]. A major challenge of micro-electro-mechanical systems (MEMS) position estimation concerns how to restrain the rapid accumulation of navigation errors.…”