Inertial navigation system

About: Inertial navigation system is a research topic. Over the lifetime, 14582 publications have been published within this topic receiving 190618 citations. The topic is also known as: intertial guidance system & inertial reference platform.

Robust INS/GPS Sensor Fusion for UAV Localization Using SDRE Nonlinear Filtering

Abstract: The aim of this paper is to present a new INS/GPS sensor fusion scheme, based on state-dependent Riccati equation (SDRE) nonlinear filtering, for unmanned aerial vehicle (UAV) localization problem. SDRE navigation filter is proposed as an alternative to extended Kalman filter (EKF), which has been largely used in the literature. Based on optimal control theory, SDRE filter solves issues linked with EKF filter such as linearization errors, which severely decrease UAV localization performances. Stability proof of SDRE nonlinear filter is also presented and validated on a 3-D UAV flight scenario. Results obtained by SDRE navigation filter were compared to EKF navigation filter results. This comparison shows better UAV localization performance using SDRE filter. The suitability of the SDRE navigation filter over an unscented Kalman navigation filter for highly nonlinear UAV flights is also demonstrated.

Vision and inertial sensor cooperation using gravity as a vertical reference

Abstract: This paper explores the combination of inertial sensor data with vision. Visual and inertial sensing are two sensory modalities that can be explored to give robust solutions on image segmentation and recovery of 3D structure from images, increasing the capabilities of autonomous robots and enlarging the application potential of vision systems. In biological systems, the information provided by the vestibular system is fused at a very early processing stage with vision, playing a key role on the execution of visual movements such as gaze holding and tracking, and the visual cues aid the spatial orientation and body equilibrium. In this paper, we set a framework for using inertial sensor data in vision systems, and describe some results obtained. The unit sphere projection camera model is used, providing a simple model for inertial data integration. Using the vertical reference provided by the inertial sensors, the image horizon line can be determined. Using just one vanishing point and the vertical, we can recover the camera's focal distance and provide an external bearing for the system's navigation frame of reference. Knowing the geometry of a stereo rig and its pose from the inertial sensors, the collineations of level planes can be recovered, providing enough restrictions to segment and reconstruct vertical features and leveled planar patches.

Attitude estimation on SO[3] based on direct inertial measurements

Abstract: This paper considers the question of obtaining high quality attitude estimates from typical low cost inertial measurement units for applications in control of unmanned aerial vehicles. A nonlinear complimentary filter exploiting the structure of special orthogonal group S0(3) is proposed. The filter is expressed explicitly in terms of direct and untreated measurements. For a typical low cost inertial measurement where two inertial directions are measured (gravitational and magnetic fields) along with angular velocity, it is shown that the filter is well conditioned. If only a single direction is available (typically the gravitational field) along with angular velocity, it is shown that the full gyro bias vector is correctly estimated and that the estimated orientation converges to a set consistent with the measurements. Experimental results, for flight data from the HoverEyecopy UAV, demonstrate the efficiency of the proposed filter

Microresonator Brillouin gyroscope

Abstract: Optical-based rotation sensors have revolutionized precision, high-sensitivity inertial navigation systems. At the same time these sensors use bulky optical fiber spools or free-space resonators. A chip-based, micro-optical gyroscope is demonstrated that uses counterpropagating Brillouin lasers to measure rotation as a Sagnac-induced frequency shift. Preliminary work has demonstrated a rotation-rate measurement that surpasses prior micro-optical rotation-sensing systems by over 40-fold.

UPTIME: Ubiquitous pedestrian tracking using mobile phones

Abstract: The mission of tracking a pedestrian is valuable for many applications including walking distance estimation for the purpose of pervasive healthcare, museum and shopping mall guides, and locating emergency responders. In this paper, we show how accurate and ubiquitous tracking of a pedestrian can be performed using only the inertial sensors embedded in his/her mobile phone. Our work depends on performing dead reckoning to track the user's movement. The main challenge that needs to be addressed is handling the noise of the low cost low quality inertial sensors in cell phones. Our proposed system combines two novel contributions: a novel step count estimation technique and a gait-based accurate variable step size detection algorithm. The step count estimation technique is based on a lightweight finite state machine approach that leverages orientation-independent features. In order to capture the varying stride length of the user, based on his changing gait, we employ a multi-class hierarchical Support Vector Machine classifier. Combining the estimated number of steps with the an accurate estimate of the individual stride length, we achieve ubiquitous and accurate tracking of a person in indoor environments. We implement our system on different Android-based phones and compare it to the state-of-the-art techniques in indoor and outdoor testbeds with arbitrary phone orientation. Our results in two different testbeds show that we can provide an accurate step count estimation with an error of 5.72%. In addition, our gait type classifier has an accuracy of 97.74%. This leads to a combined tracking error of 6.9% while depending only on the inertial sensors and turning off the GPS sensor completely. This highlights the ability of the system to provide ubiquitous, accurate, and energy efficient tracking.