Feature-based probabilistic map building using time and amplitude information of sonar in indoor environments

Robotica ◽  
2001 ◽  
Vol 19 (4) ◽  
pp. 423-437 ◽  
Author(s):  
Hyoung Jo Jeon ◽  
Byung Kook Kim
Keyword(s):  
Kalman Filter ◽  
Conditional Probability ◽  
Extended Kalman Filter ◽  
Indoor Environment ◽  
Experimental Results ◽  
Indoor Environments ◽  
Map Building ◽  
Target Groups ◽  
Feature Based ◽  
Probabilistic Map

We present a feature-based probabilistic map building algorithm which directly utilizes time and amplitude information of sonar in indoor environments. Utilizing additional amplitude-of-signal (AOS) obtained concurrently with time-of-flight (TOF), the amount of inclination of target can be directly calculated from a single echo, and the number of measurements can be greatly reduced with result similar to dense scanning. A set of target groups (set of hypothesized targets originated from one measurement) is used and refined by each measurement using an extended Kalman filter and Bayesian conditional probability. Experimental results in a real indoor environment are presented to show the validity of our algorithm.

Influence analysis of measurement errors in satellite attitude determination based on extended Kalman filter

2012 ◽  
Vol 116 (1178) ◽  
pp. 373-389
Author(s):  
Y. Jiao ◽  
J. Wang ◽  
X. Pan ◽  
H. Zhou
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Measurement Errors ◽  
Attitude Determination ◽  
Previous Analysis ◽  
Measurement Model ◽  
Experimental Results ◽  
Influence Analysis ◽  
Satellite Attitude ◽  
Structural Error

Abstract The satellite attitude determination approach based on the Extended Kalman Filter (EKF) has been widely used in many real applications. However, the accuracy of this method largely depends on the fitness of measurement model. We aim to analyse the influence of measurement errors to the accuracy of EKF based attitude determination approach in this paper. The measurement errors, which are divided into structural error and nonstructural error by their influences, are analysed in principle. In the setting of the combination of star sensors and gyros, according to the property of innovation, we employ the technique of correlation test to analyse the influences of different kinds of measurement errors. Experimental results demonstrate the effectiveness of our previous analysis.

An Extended Kalman Filter Algorithm for a PMSM: Experimental Results

2016 ◽  
Vol 10 (1) ◽  
pp. 32-38
Author(s):  
O. Asseu ◽  
P. Yoboue ◽  
A. Konate ◽  
M. Diaby
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Experimental Results ◽  
Kalman Filter Algorithm

Fast Kalman SLAM

2010 ◽  
Vol 44-47 ◽  
pp. 3174-3179
Author(s):  
Wu Zhou ◽  
Chun Xia Zhao ◽  
Mian Hao Zhang
Keyword(s):  
Kalman Filter ◽  
Computational Complexity ◽  
Extended Kalman Filter ◽  
Simulation Analysis ◽  
Unscented Kalman Filter ◽  
Estimation Accuracy ◽  
Map Building ◽  
Complex Environments ◽  
Path Component ◽  
High Computational Complexity

When Simultaneous Localization and Map Building is carried out in complex environments, reduction of computational complexity is a key problem. With a view to the high computational complexity of particle filter, a SLAM solution named ‘Fast Kalman SLAM’ is introduced. Adopting the ‘decomposition’ idea in the FastSLAM algorithm, Fast Kalman SLAM factors the joint SLAM state into a path component and a conditional map component. The robot pose is estimated recursively with Mean Extended Kalman Filter (MEKF) or Unscented Kalman Filter (UKF), while the map with Extended Kalman Filter (EKF). Simulative experiments are carried out to evaluate the performance of the presented algorithm. And Simulation analysis is made for the presented algorithm. The experimental results indicate that the new algorithm reduces computational complexity greatly and ensures estimation accuracy at the same time.

scholarly journals Simultaneous localization and mapping: A feature-based probabilistic approach

2009 ◽  
Vol 19 (4) ◽  
pp. 575-588 ◽  
Author(s):  
Piotr Skrzypczyński
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Data Association ◽  
Probabilistic Approach ◽  
Simultaneous Localization And Mapping ◽  
Probabilistic Formulation ◽  
Recent Advances ◽  
Localization And Mapping ◽  
Feature Based ◽  
Points Of View

Simultaneous localization and mapping: A feature-based probabilistic approachThis article provides an introduction to Simultaneous Localization And Mapping (SLAM), with the focus on probabilistic SLAM utilizing a feature-based description of the environment. A probabilistic formulation of the SLAM problem is introduced, and a solution based on the Extended Kalman Filter (EKF-SLAM) is shown. Important issues of convergence, consistency, observability, data association and scaling in EKF-SLAM are discussed from both theoretical and practical points of view. Major extensions to the basic EKF-SLAM method and some recent advances in SLAM are also presented.

scholarly journals UWB/PDR Tightly Coupled Navigation with Robust Extended Kalman Filter for NLOS Environments

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Xin Li ◽  
Yan Wang ◽  
Kourosh Khoshelham
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Dead Reckoning ◽  
Step Length ◽  
Noise Signal ◽  
Ultra Wideband ◽  
Measurement Unit ◽  
Indoor Environments ◽  
Fusion Algorithm ◽  
Tightly Coupled

The fusion of ultra-wideband (UWB) and inertial measurement unit (IMU) is an effective solution to overcome the challenges of UWB in nonline-of-sight (NLOS) conditions and error accumulation of inertial positioning in indoor environments. However, existing systems are based on foot-mounted or body-worn IMUs, which limit the application of the system to specific practical scenarios. In this paper, we propose the fusion of UWB and pedestrian dead reckoning (PDR) using smartphone IMU, which has the potential to provide a universal solution to indoor positioning. The PDR algorithm is based on low-pass filtering of acceleration data and time thresholding to estimate the step length. According to different movement patterns of pedestrians, such as walking and running, several step models are comparatively analyzed to determine the appropriate model and related parameters of the step length. For the PDR direction calculation, the Madgwick algorithm is adopted to improve the calculation accuracy of the heading algorithm. The proposed UWB/PDR fusion algorithm is based on the extended Kalman filter (EKF), in which the Mahalanobis distance from the observation to the prior distribution is used to suppress the influence of abnormal UWB data on the positioning results. Experimental results show that the algorithm is robust to the intermittent noise, continuous noise, signal interruption, and other abnormalities of the UWB data.

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