Basic Extended Kalman Filter – Simultaneous Localisation and Mapping

Matsebe, Oduetse; Namoshe, Molaletsa; Tlale, N.S.

doi:10.5772/9255

Cited by 3 publications

(2 citation statements)

References 5 publications

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“…The second variant excludes only the velocity feedback from the LbPM as a source of error. The presented method is based on [ 57 , 58 , 59 , 60 ], and the specifics applicable to this work are detailed in what follows.…”

Section: Methodsmentioning

confidence: 99%

High Precision Outdoor and Indoor Reference State Estimation for Testing Autonomous Vehicles

Morales

Dauth

Huber³

et al. 2021

Sensors

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A current trend in automotive research is autonomous driving. For the proper testing and validation of automated driving functions a reference vehicle state is required. Global Navigation Satellite Systems (GNSS) are useful in the automation of the vehicles because of their practicality and accuracy. However, there are situations where the satellite signal is absent or unusable. This research work presents a methodology that addresses those situations, thus largely reducing the dependency of Inertial Navigation Systems (INSs) on the SatNav. The proposed methodology includes (1) a standstill recognition based on machine learning, (2) a detailed mathematical description of the horizontation of inertial measurements, (3) sensor fusion by means of statistical filtering, (4) an outlier detection for correction data, (5) a drift detector, and (6) a novel LiDAR-based Positioning Method (LbPM) for indoor navigation. The robustness and accuracy of the methodology are validated with a state-of-the-art INS with Real-Time Kinematic (RTK) correction data. The results obtained show a great improvement in the accuracy of vehicle state estimation under adverse driving conditions, such as when the correction data is corrupted, when there are extended periods with no correction data and in the case of drifting. The proposed LbPM method achieves an accuracy closely resembling that of a system with RTK.

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Section: Methodsmentioning

confidence: 99%

High Precision Outdoor and Indoor Reference State Estimation for Testing Autonomous Vehicles

Morales

Dauth

Huber³

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…In an effort to solve the probability distribution (5), using a recursive equation that allows the system to incorporate an observation and one control at a time and estimate the current state of system recursively [30]. In addition, Extended Kalman Filter (EKF) is a recursive Bayes filter and this recursive equation can be divided into two steps [31], [32]. First, prediction step that estimates a new position of robot by taking into account the control command which was executed as well as current position of robot (8), and second step is called correction which considers current sensor observation along with predicted observation then based on the discrepancy of them and normalization constant , correct the new state (9).…”

Section: Extended Kalman Filtermentioning

confidence: 99%

Three main paradigms of simultaneous localization and mapping (SLAM) problem

Imani

Haataja

Toivanen

2018

Tenth International Conference on Machine Vision (ICMV 2017)

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Simultaneous Localization and Mapping (SLAM) is one of the most challenging research areas within computer and machine vision for automated scene commentary and explanation. The SLAM technique has been a developing research area in the robotics context during recent years. By utilizing the SLAM method robot can estimate the different positions of the robot at the distinct points of time which can indicate the trajectory of robot as well as generate a map of the environment. SLAM has unique traits which are estimating the location of robot and building a map in the various types of environment. SLAM is effective in different types of environment such as indoor, outdoor district, Air, Underwater, Underground and Space. Several approaches have been investigated to use SLAM technique in distinct environments. The purpose of this paper is to provide an accurate perceptive review of case history of SLAM relied on laser/ultrasonic sensors and camera as perception input data. In addition, we mainly focus on three paradigms of SLAM problem with all its pros and cons. In the future, use intelligent methods and some new idea will be used on visual SLAM to estimate the motion intelligent underwater robot and building a feature map of marine environment.

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An implementation of SLAM with extended Kalman filter

Saman

Lotfy

2016

2016 6th International Conference on Intelligent and Advanced Systems (ICIAS)

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Basic Extended Kalman Filter – Simultaneous Localisation and Mapping

Cited by 3 publications

References 5 publications

High Precision Outdoor and Indoor Reference State Estimation for Testing Autonomous Vehicles

High Precision Outdoor and Indoor Reference State Estimation for Testing Autonomous Vehicles

Three main paradigms of simultaneous localization and mapping (SLAM) problem

An implementation of SLAM with extended Kalman filter

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