Comparison of different SLAM approaches for a driverless race car

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Nick Le Large ◽  
Frank Bieder ◽  
Martin Lauer
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Ground Truth ◽  
Time Constraints ◽  
Differential Gps ◽  
Real World Data ◽  
Race Car ◽  
Localization And Mapping ◽  
Slam Algorithm ◽  
Computational Resources

Abstract For the application of an automated, driverless race car, we aim to assure high map and localization quality for successful driving on previously unknown, narrow race tracks. To achieve this goal, it is essential to choose an algorithm that fulfills the requirements in terms of accuracy, computational resources and run time. We propose both a filter-based and a smoothing-based Simultaneous Localization and Mapping (SLAM) algorithm and evaluate them using real-world data collected by a Formula Student Driverless race car. The accuracy is measured by comparing the SLAM-generated map to a ground truth map which was acquired using high-precision Differential GPS (DGPS) measurements. The results of the evaluation show that both algorithms meet required time constraints thanks to a parallelized architecture, with GraphSLAM draining the computational resources much faster than Extended Kalman Filter (EKF) SLAM. However, the analysis of the maps generated by the algorithms shows that GraphSLAM outperforms EKF SLAM in terms of accuracy.

scholarly journals Simultaneous Localization and Mapping Based on Kalman Filter and Extended Kalman Filter

2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Inam Ullah ◽  
Xin Su ◽  
Xuewu Zhang ◽  
Dongmin Choi
Keyword(s):  
Kalman Filter ◽  
Mobile Robot ◽  
Extended Kalman Filter ◽  
Simultaneous Localization And Mapping ◽  
Absolute Measurement ◽  
Relative Measurement ◽  
Localization Algorithm ◽  
Moving Vehicle ◽  
Localization And Mapping ◽  
Slam Algorithm

For more than two decades, the issue of simultaneous localization and mapping (SLAM) has gained more attention from researchers and remains an influential topic in robotics. Currently, various algorithms of the mobile robot SLAM have been investigated. However, the probability-based mobile robot SLAM algorithm is often used in the unknown environment. In this paper, the authors proposed two main algorithms of localization. First is the linear Kalman Filter (KF) SLAM, which consists of five phases, such as (a) motionless robot with absolute measurement, (b) moving vehicle with absolute measurement, (c) motionless robot with relative measurement, (d) moving vehicle with relative measurement, and (e) moving vehicle with relative measurement while the robot location is not detected. The second localization algorithm is the SLAM with the Extended Kalman Filter (EKF). Finally, the proposed SLAM algorithms are tested by simulations to be efficient and viable. The simulation results show that the presented SLAM approaches can accurately locate the landmark and mobile robot.

scholarly journals A Bioinspired Neural Model Based Extended Kalman Filter for Robot SLAM

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Jianjun Ni ◽  
Chu Wang ◽  
Xinnan Fan ◽  
Simon X. Yang
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Neural Model ◽  
Noise Model ◽  
Model Based ◽  
Localization And Mapping ◽  
Observation Noise ◽  
Slam Algorithm ◽  
The Stability ◽  
Estimated Error

Robot simultaneous localization and mapping (SLAM) problem is a very important and challenging issue in the robotic field. The main tasks of SLAM include how to reduce the localization error and the estimated error of the landmarks and improve the robustness and accuracy of the algorithms. The extended Kalman filter (EKF) based method is one of the most popular methods for SLAM. However, the accuracy of the EKF based SLAM algorithm will be reduced when the noise model is inaccurate. To solve this problem, a novel bioinspired neural model based SLAM approach is proposed in this paper. In the proposed approach, an adaptive EKF based SLAM structure is proposed, and a bioinspired neural model is used to adjust the weights of system noise and observation noise adaptively, which can guarantee the stability of the filter and the accuracy of the SLAM algorithm. The proposed approach can deal with the SLAM problem in various situations, for example, the noise is in abnormal conditions. Finally, some simulation experiments are carried out to validate and demonstrate the efficiency of the proposed approach.

Performance Comparison of Inertial Measurement Units Fused With Odometry in Extended Kalman Filter for Dead-Reckoning Navigation

2019 ◽  
Author(s):  
Kevin Carey ◽  
Benjamin Abruzzo ◽  
David P. Harvie ◽  
Christopher Korpela
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Autonomous Vehicle ◽  
Dead Reckoning ◽  
Ground Truth ◽  
Performance Comparison ◽  
Inertial Measurement Units ◽  
Ground Vehicle ◽  
Inertial Measurement ◽  
Measurement Units

Abstract This paper aims to aid robot and autonomous vehicle designers by providing a comparison between four different inertial measurement units (IMUs) which could be used to aid in vehicle navigation in a GPS-denied or inertial-only scenario. A differential-drive ground vehicle was designed to carry the multiple different IMUs, mounted coaxially, to enable direct comparison of performance in a planar environment. The experiments focused on the growth of pose error of the ground vehicle originating from the odometry senors and the IMUs. An extended Kalman Filter was developed to fuse the odometry and inertial measurements for this comparison. The four specific IMUs evaluated were: CNS 5000, Xsens 300, Microstrain GX5-35, and Phidgets 1044 and the ground truth for experiments was provided by an Optitrack motion capture system (MCS). Finally, metrics for choosing IMUs, merging cost and performance considerations, are proposed and discussed. While the CNS 5000 has the best objective error specifications, based on these metrics the Xsens 300 exhibits the best absolute performance while the Phidgets 1044 provides the best performance-per-dollar.

scholarly journals A hypothesis of state covariance decorrelation effects to partial observability SLAM

2019 ◽  
Vol 14 (2) ◽  
pp. 588 ◽  
Author(s):  
H Ahmad ◽  
N.A Othman ◽  
M M Saari ◽  
M S Ramli ◽  
M M Mazlan ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Matrix Inversion ◽  
Matrix Norm ◽  
Main Element ◽  
Partial Observability ◽  
Localization And Mapping ◽  
The Matrix ◽  
Simulation Results ◽  
Partially Observable

<span>This paper analyze the performance of partial observability in simultaneous localization and mapping(SLAM) problem. The study focuses mainly on the effect of having a decorrelation technique known as Covariance Inflation to the estimation. The matrix inversion will be the main element to be investigated through two conditions with respect to some defined environment namely as unstable partially observable SLAM and partially observable SLAM via matrix norm analysis. For assessment purposes, the Extended Kalman Filter estimation is referred as the estimator to understand how the conditions can influence the results. The simulation results depicted that, the matrix norm is able to determine the efficiency of estimation and is proportional to the uncertainties of the system.</span>

scholarly journals An Adaptive UKF Based SLAM Method for Unmanned Underwater Vehicle

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Hongjian Wang ◽  
Guixia Fu ◽  
Juan Li ◽  
Zheping Yan ◽  
Xinqian Bian
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Estimation Error ◽  
Underwater Vehicle ◽  
Statistical Parameters ◽  
Sea Trial ◽  
Unmanned Underwater Vehicle ◽  
Noise Statistic ◽  
Localization And Mapping ◽  
Slam Algorithm

This work proposes an improved unscented Kalman filter (UKF)-based simultaneous localization and mapping (SLAM) algorithm based on an adaptive unscented Kalman filter (AUKF) with a noise statistic estimator. The algorithm solves the issue that conventional UKF-SLAM algorithms have declining accuracy, with divergence occurring when the prior noise statistic is unknown and time-varying. The new SLAM algorithm performs an online estimation of the statistical parameters of unknown system noise by introducing a modified Sage-Husa noise statistic estimator. The algorithm also judges whether the filter is divergent and restrains potential filtering divergence using a covariance matching method. This approach reduces state estimation error, effectively improving navigation accuracy of the SLAM system. A line feature extraction is implemented through a Hough transform based on the ranging sonar model. Test results based on unmanned underwater vehicle (UUV) sea trial data indicate that the proposed AUKF-SLAM algorithm is valid and feasible and provides better accuracy than the standard UKF-SLAM system.

A NOVEL ADAPTIVE NEURO-FUZZY UNSCENTED KALMAN FILTER FOR SLAM

2011 ◽  
Vol 08 (01) ◽  
pp. 223-243 ◽  
Author(s):  
RAMAZAN HAVANGI ◽  
MOHAMMAD TESHNEHLAB ◽  
MOHAMMAD ALI NEKOUI
Keyword(s):  
Kalman Filter ◽  
Fuzzy Inference ◽  
Unscented Kalman Filter ◽  
A Priori ◽  
Inference System ◽  
Localization And Mapping ◽  
Neuro Fuzzy ◽  
Measurement Noise Covariance ◽  
Slam Algorithm ◽  
Sampling Instant

Extended Kalman filter (EKF) has been used as a popular choice to solve simultaneous localization and mapping (SLAM) problem. However, SLAM algorithm based on EKF-SLAM has two serious drawbacks, namely the linear approximation of nonlinear functions and the calculation of Jacobin matrices. For solving these problems, SLAM algorithm based on unscented Kalman filter (UKF-SLAM) has been recently proposed. However, the performance of the UKF-SLAM and thus the quality of the estimation depends on the correct a priori knowledge of process and measurement noise covariance matrices respectively denoted by Qk and Rk. Imprecise knowledge of these statistics can cause significant degradation in performance. This article proposes the development of an adaptive neuro-fuzzy UKF (ANFUKF) for SLAM. The Adaptive neuro-fuzzy attempts to estimate the elements of Rk matrix in the UKF-SLAM algorithm at each sampling instant when measurement updating step is carried out. The adaptive neuro-fuzzy inference system (ANFIS) supervises the performance of the UKF-SLAM with the aim of reducing the mismatch between the theoretical and actual covariance of the innovation sequences. The free parameters of ANFIS are trained using the steepest gradient descent (GD) to minimize the differences of the actual value of the covariance of the residual with its theoretical value as much as possible. The simulation results show the effectiveness of the proposed algorithm.

scholarly journals Straight-lines modelling using planar information for monocular SLAM

2012 ◽  
Vol 22 (2) ◽  
pp. 409-421 ◽  
Author(s):  
André Santana ◽  
Adelardo Medeiros
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Hough Transform ◽  
Simultaneous Localization And Mapping ◽  
Intermediate Stage ◽  
Localization And Mapping ◽  
Monocular Slam ◽  
Real Robot ◽  
Straight Lines ◽  
Indoor And Outdoor

Straight-lines modelling using planar information for monocular SLAMThis work proposes a SLAM (Simultaneous Localization And Mapping) solution based on an Extended Kalman Filter (EKF) in order to enable a robot to navigate along the environment using information from odometry and pre-existing lines on the floor. These lines are recognized by a Hough transform and are mapped into world measurements using a homography matrix. The prediction phase of the EKF is developed using an odometry model of the robot, and the updating makes use of the line parameters in Kalman equations without any intermediate stage for calculating the distance or the position. We show two experiments (indoor and outdoor) dealing with a real robot in order to validate the project.

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.

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