scholarly journals Attitude Estimation for Dynamic Legged Locomotion Using Range and Inertial Sensors

2006 ◽  
Author(s):  
S.P.N. Singh ◽  
K.J. Waldron
Keyword(s):  
Inertial Sensors ◽  
Legged Locomotion ◽  
Attitude Estimation

A novel approach for attitude estimation using MEMS inertial sensors

2014 ◽  
Author(s):  
Zheming Wu ◽  
Zhenguo Sun ◽  
Wenzeng Zhang ◽  
Qiang Chen
Keyword(s):  
Inertial Sensors ◽  
Attitude Estimation ◽  
Novel Approach

Least square estimation-based adaptive complimentary filter for attitude estimation

2018 ◽  
Vol 41 (1) ◽  
pp. 235-245 ◽  
Author(s):  
Parag Narkhede ◽  
Alex Noel Joseph Raj ◽  
Vipan Kumar ◽  
Vinod Karar ◽  
Shashi Poddar
Keyword(s):  
Inertial Sensors ◽  
Low Cost ◽  
Attitude Estimation ◽  
Unmanned Vehicles ◽  
Least Square ◽  
Least Square Estimation ◽  
Micro Electro Mechanical Systems ◽  
Moving Vehicle ◽  
Complementary Filter ◽  
Filter Structure

Attitude estimation is one of the core fundamentals for navigation of unmanned vehicles and other robotic systems. With the advent of low cost and low accuracy micro-electro-mechanical systems (MEMS) based inertial sensors, these devices are used ubiquitously for all such commercial grade systems that need motion information. However, these sensors suffer from time-varying bias and noise parameters, which need to be compensated during system state estimation. Complementary filtering is one of such techniques that is used here for estimating attitude of a moving vehicle. However, the complementary filter structure is dependent on user fed gain parameters, KP and KI and needs a mechanism by which they can be obtained automatically. In this paper, an attempt has been made towards addressing this issue by applying least square estimation technique on the error obtained between estimated and measured attitude angles. The proposed algorithm simplifies the design of nonlinear complementary filter by computing the filter gains automatically. The experimental investigation has been carried out over several datasets, confirming the advantage of obtaining gain parameters automatically for the complementary filtering structure.

Attitude Estimation using Fusion of Monocular SLAM and Inertial Sensors

2014 ◽  
Vol 12 (6) ◽  
pp. 977-984 ◽  
Author(s):  
Carlos Vianchada Estevez ◽  
Ponciano Jorge Escamilla Ambrosio ◽  
Mariana Natalia Ibarra Bonilla ◽  
Juan Manuel Ramirez Cortes ◽  
Pilar Gomez Gil
Keyword(s):  
Inertial Sensors ◽  
Attitude Estimation ◽  
Monocular Slam

scholarly journals Performance Trades for Multiantenna GNSS Multisensor Attitude Determination Systems

2018 ◽  
Vol 2018 ◽  
pp. 1-12
Author(s):  
Nathan A. Tehrani ◽  
Jason N. Gross
Keyword(s):  
Attitude Determination ◽  
Inertial Sensors ◽  
Satellite System ◽  
Attitude Estimation ◽  
Multiple Antenna ◽  
Estimation Performance ◽  
Trade Offs ◽  
Sensing Platforms ◽  
Global Navigation Satellite ◽  
System Configurations

We present various performance trades for multiantenna global navigation satellite system (GNSS) multisensor attitude estimation systems. In particular, attitude estimation performance sensitivity to various error sources and system configurations is assessed. This study is motivated by the need for system designers, scientists, and engineers of airborne astronomical and remote sensing platforms to better determine which system configuration is most suitable for their specific application. In order to assess performance trade-offs, the attitude estimation performance of various approaches is tested using a simulation that is based on a stratospheric balloon platform. For GNSS errors, attention is focused on multipath, receiver measurement noise, and carrier-phase breaks. For the remaining attitude sensors, different performance grades of sensors are assessed. Through a Monte Carlo simulation, it is shown that, under typical conditions, sub-0.1-degree attitude accuracy is available when using multiple antenna GNSS data only, but that this accuracy can degrade to degree level in some environments warranting the inclusion of additional attitude sensors to maintain the desired level of accuracy. Further, we show that integrating inertial sensors is more valuable whenever accurate pitch and roll estimates are critical.

scholarly journals A Robust Nonlinear Observer for Real-Time Attitude Estimation Using Low-Cost MEMS Inertial Sensors

Sensors ◽  
2013 ◽  
Vol 13 (11) ◽  
pp. 15138-15158 ◽  
Author(s):  
José Guerrero-Castellanos ◽  
Heberto Madrigal-Sastre ◽  
Sylvain Durand ◽  
Lizeth Torres ◽  
German Muñoz-Hernández
Keyword(s):  
Real Time ◽  
Inertial Sensors ◽  
Low Cost ◽  
Attitude Estimation ◽  
Nonlinear Observer ◽  
Robust Nonlinear Observer

scholarly journals Nonlinear Complementary Filter for Attitude Estimation by Fusing Inertial Sensors and a Camera

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6752
Author(s):  
Lingxiao Zheng ◽  
Xingqun Zhan ◽  
Xin Zhang
Keyword(s):  
Inertial Sensors ◽  
Filter Design ◽  
Low Cost ◽  
Measurement Model ◽  
Attitude Estimation ◽  
System Stability ◽  
Feature Points ◽  
Complementary Filter ◽  
Geometry Measurement ◽  
Implicit Geometry

Using a standalone camera for pose estimation has been quite a standard task. However, the point correspondence-based algorithms require at least four feature points in the field of view. This paper considers the situation that there are only two feature points. Focusing on the attitude estimation, we propose to fuse a camera with low-cost inertial sensors based on a nonlinear complementary filter design. An implicit geometry measurement model is derived using two feature points in an image. This geometry measurement is fused with the angle rate measurement and vector measurement from inertial sensors using the proposed nonlinear complementary filter with only two parameters to be adjusted. The proposed nonlinear complementary filter is posed directly on the special orthogonal group SO(3). Based on the theory of nonlinear system stability analysis, the proposed filter ensures locally asymptotic stability. A quaternion-based discrete implementation of the filter is also given in this paper for computational efficiency. The proposed algorithm is validated using a smartphone with built-in inertial sensors and a rear camera. The experimental results indicate that the proposed algorithm outperforms all the compared counterparts in estimated accuracy and provides competitive computational complexity.

Attitude Estimation for a Vehicle Using Inertial Sensors

1993 ◽  
Vol 26 (1) ◽  
pp. 89-94 ◽  
Author(s):  
J. Vaganay ◽  
M.J. Aldon
Keyword(s):  
Inertial Sensors ◽  
Attitude Estimation

Attitude estimation for a vehicle using inertial sensors

1994 ◽  
Vol 2 (2) ◽  
pp. 281-287 ◽  
Author(s):  
J. Vaganay ◽  
M.J. Aldon
Keyword(s):  
Inertial Sensors ◽  
Attitude Estimation
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