A multi-innovation with forgetting factor based EKF-SLAM method for mobile robots

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Zhen Zhou ◽  
Dongqing Wang ◽  
Boyang Xu
Keyword(s):  
Mobile Robots ◽  
Strong Nonlinearity ◽  
Forgetting Factor ◽  
Extended Kalman Filtering ◽  
Estimation Errors ◽  
Content Type ◽  
Application Range ◽  
Localization And Mapping ◽  
Slam Algorithm ◽  
Filtering Effect

Purpose The purpose of this paper is to explore a multi-innovation with forgetting factor-based EKF-SLAM (FMI-EKF-SLAM) algorithm to solve the error increasing problem, caused by the Extended Kalman filtering (EKF) violating the local linear assumption in simultaneous localization and mapping (SLAM) for mobile robots because of strong nonlinearity. Design/methodology/approach A multi-innovation with forgetting factor-based EKF-SLAM (FMI-EKF-SLAM) algorithm is investigated. At each filtering step, the FMI-EKF-SLAM algorithm expands the single innovation at current step to an extended multi-innovation containing current and previous steps and introduces the forgetting factor to reduce the effect of old innovations. Findings The simulation results show that the explored FMI-EKF-SLAM method reduces the state estimation errors, obtains the ideal filtering effect and achieves higher accuracy in positioning and mapping. Originality/value The method proposed in this paper improves the positioning accuracy of SLAM and improves the EKF, so that the EKF has higher accuracy and wider application range.

Differential evolution tuned fuzzy supervisor adapted extended Kalman filtering for SLAM problems in mobile robots

Robotica ◽  
2009 ◽  
Vol 27 (3) ◽  
pp. 411-423 ◽  
Author(s):  
Amitava Chatterjee
Keyword(s):  
Differential Evolution ◽  
Mobile Robots ◽  
Autonomous Vehicles ◽  
Extended Kalman Filtering ◽  
Extended Kalman Filters ◽  
De Algorithm ◽  
Localization And Mapping ◽  
Recent Method ◽  
Fuzzy Supervisor

SUMMARYThe present paper proposes a successful application of differential evolution (DE) optimized fuzzy logic supervisors (FLS) to improve the quality of solutions that extended Kalman filters (EKFs) can offer to solve simultaneous localization and mapping (SLAM) problems for mobile robots and autonomous vehicles. The utility of the proposed system can be readily appreciated in those situations where an incorrect knowledge of Q and R matrices of EKF can significantly degrade the SLAM performance. A fuzzy supervisor has been implemented to adapt the R matrix of the EKF online, in order to improve its performance. The free parameters of the fuzzy supervisor are suitably optimized by employing the DE algorithm, a comparatively recent method, popularly employed now-a-days for high-dimensional parallel direct search problems. The utility of the proposed system is aptly demonstrated by solving the SLAM problem for a mobile robot with several landmarks and with wrong knowledge of sensor statistics. The system could successfully demonstrate enhanced performance in comparison with usual EKF-based solutions for identical environment situations.

A scan matching simultaneous localization and mapping algorithm based on particle filter

2016 ◽  
Vol 43 (6) ◽  
pp. 607-616 ◽  
Author(s):  
Hui Xiong ◽  
Youping Chen ◽  
Xiaoping Li ◽  
Bing Chen ◽  
Jun Zhang
Keyword(s):  
Particle Filter ◽  
Hill Climbing ◽  
Closure Problem ◽  
Memory Consumption ◽  
Scan Matching ◽  
Loop Closure ◽  
Content Type ◽  
Grid Map ◽  
Localization And Mapping ◽  
Slam Algorithm

Purpose The purpose of this paper is to present a scan matching simultaneous localization and mapping (SLAM) algorithm based on particle filter to generate the grid map online. It mainly focuses on reducing the memory consumption and alleviating the loop closure problem. Design/methodology/approach The proposed method alleviates the loop closure problem by improving the accuracy of the robot’s pose. First, two improvements were applied to enhance the accuracy of the hill climbing scan matching. Second, a particle filter was used to maintain the diversity of the robot’s pose and then to supply potential seeds to the hill climbing scan matching to ensure that the best match point was the global optimum. The proposed method reduces the memory consumption by maintaining only a single grid map. Findings Simulation and experimental results have proved that this method can build a consistent map of a complex environment. Meanwhile, it reduced the memory consumption and alleviates the loop closure problem. Originality/value In this paper, a new SLAM algorithm has been proposed. It can reduce the memory consumption and alleviate the loop closure problem without lowering the accuracy of the generated grid map.

Rao-Blackwellized visual SLAM for small UAVs with vehicle model partition

2014 ◽  
Vol 41 (3) ◽  
pp. 266-274 ◽  
Author(s):  
Tianmiao Wang ◽  
Chaolei Wang ◽  
Jianhong Liang ◽  
Yicheng Zhang
Keyword(s):  
Particle Filter ◽  
Internal State ◽  
Factorization Method ◽  
Measurement Unit ◽  
Visual Slam ◽  
Vehicle Model ◽  
Theoretical Derivation ◽  
Content Type ◽  
Localization And Mapping ◽  
Slam Algorithm

Purpose – The purpose of this paper is to present a Rao–Blackwellized particle filter (RBPF) approach for the visual simultaneous localization and mapping (SLAM) of small unmanned aerial vehicles (UAVs). Design/methodology/approach – Measurements from inertial measurement unit, barometric altimeter and monocular camera are fused to estimate the state of the vehicle while building a feature map. In this SLAM framework, an extra factorization method is proposed to partition the vehicle model into subspaces as the internal and external states. The internal state is estimated by an extended Kalman filter (EKF). A particle filter is employed for the external state estimation and parallel EKFs are for the map management. Findings – Simulation results indicate that the proposed approach is more stable and accurate than other existing marginalized particle filter-based SLAM algorithms. Experiments are also carried out to verify the effectiveness of this SLAM method by comparing with a referential global positioning system/inertial navigation system. Originality/value – The main contribution of this paper is the theoretical derivation and experimental application of the Rao–Blackwellized visual SLAM algorithm with vehicle model partition for small UAVs.

A new compensation technique based on analysis of resampling process in FastSLAM

Robotica ◽  
2008 ◽  
Vol 26 (2) ◽  
pp. 205-217 ◽  
Author(s):  
Nosan Kwak ◽  
Gon-Woo Kim ◽  
Beom-Hee Lee
Keyword(s):  
Genetic Algorithms ◽  
Mobile Robots ◽  
Performance Measures ◽  
State Of The Art ◽  
Effective Sample Size ◽  
Estimation Errors ◽  
Localization And Mapping ◽  
Compensation Technique ◽  
Run Time ◽  
Number Of Particles

SUMMARYThe state-of-the-art FastSLAM algorithm has been shown to cause a particle depletion problem while performing simultaneous localization and mapping for mobile robots. As a result, it always produces over-confident estimates of uncertainty as time progresses. This particle depletion problem is mainly due to the resampling process in FastSLAM, which tends to eliminate particles with low weights. Therefore, the number of particles to conduct loop-closure decreases, which makes the performance of FastSLAM degenerate. The resampling process has not been thoroughly analyzed even though it is the main reason for the particle depletion problem. In this paper, standard resampling algorithms (systematic residual and partial resampling), a rank-based resampling adopting genetic algorithms are analyzed using computer simulations. Several performance measures such as the effective sample size, the number of distinct particles, estimation errors, and complexity are used for the thorough analysis of the resampling algorithms. Moreover, a new compensation technique is proposed instead of resampling to resolve the particle depletion problem in FastSLAM. In estimation errors, the compensation technique outperformed other resampling algorithms though its run-time was longer than those of others. The most appropriate time to instigate compensation to reduce the run-time was also analyzed with the diminishing number of particles.

scholarly journals An FPGA Based Energy Efficient DS-SLAM Accelerator for Mobile Robots in Dynamic Environment

2021 ◽  
Vol 11 (4) ◽  
pp. 1828
Author(s):  
Yakun Wu ◽  
Li Luo ◽  
Shujuan Yin ◽  
Mengqi Yu ◽  
Fei Qiao ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Mobile Robots ◽  
Semantic Information ◽  
Dynamic Environment ◽  
High Energy ◽  
Significant Loss ◽  
Frame Rate ◽  
Energy Efficiency Improvement ◽  
Localization And Mapping ◽  
Slam Algorithm

The Simultaneous Localization and Mapping (SLAM) algorithm is a hotspot in robot application research with the ability to help mobile robots solve the most fundamental problems of “localization” and “mapping”. The visual semantic SLAM algorithm fused with semantic information enables robots to understand the surrounding environment better, thus dealing with complexity and variability of real application scenarios. DS-SLAM (Semantic SLAM towards Dynamic Environment), one of the representative works in visual semantic SLAM, enhances the robustness in the dynamic scene through semantic information. However, the introduction of deep learning increases the complexity of the system, which makes it a considerable challenge to achieve the real-time semantic SLAM system on the low-power embedded platform. In this paper, we realized the high energy-efficiency DS-SLAM algorithm on the Field Programmable Gate Array (FPGA) based heterogeneous platform through the optimization co-design of software and hardware with the help of OpenCL (Open Computing Language) development flow. Compared with Intel i7 CPU on the TUM dataset, our accelerator achieves up to 13× frame rate improvement, and up to 18× energy efficiency improvement, without significant loss in accuracy.

A two-level optimized graph-based simultaneous localization and mapping algorithm

2018 ◽  
Vol 45 (6) ◽  
pp. 758-765
Author(s):  
Hui Xiong ◽  
Youping Chen ◽  
Xiaoping Li ◽  
Bing Chen
Keyword(s):  
Simultaneous Localization And Mapping ◽  
Environmental Information ◽  
Indoor Environments ◽  
Construction Process ◽  
Loop Closure ◽  
Content Type ◽  
Mapping Algorithm ◽  
Localization And Mapping ◽  
Slam Algorithm ◽  
Estimated Error

PurposeBecause submaps including a subset of the global map contain more environmental information, submap-based graph simultaneous localization and mapping (SLAM) has been studied by many researchers. In most of those studies, helpful environmental information was not taken into consideration when designed the termination criterion of the submap construction process. After optimizing the graph, cumulative error within the submaps was also ignored. To address those problems, this paper aims to propose a two-level optimized graph-based SLAM algorithm.Design/methodology/approachSubmaps are updated by extended Kalman filter SLAM while no geometric-shaped landmark models are needed; raw laser scans are treated as landmarks. A more reasonable criterion called the uncertainty index is proposed to combine with the size of the submap to terminate the submap construction process. After a submap is completed and a loop closure is found, a two-level optimization process is performed to minimize the loop closure error and the accumulated error within the submaps.FindingsSimulation and experimental results indicate that the estimated error of the proposed algorithm is small, and the maps generated are consistent whether in global or local.Practical implicationsThe proposed method is robust to sparse pedestrians and can be adapted to most indoor environments.Originality/valueIn this paper, a two-level optimized graph-based SLAM algorithm is proposed.

scholarly journals A SLAM Algorithm Based on Adaptive Cubature Kalman Filter

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Fei Yu ◽  
Qian Sun ◽  
Chongyang Lv ◽  
Yueyang Ben ◽  
Yanwei Fu
Keyword(s):  
Kalman Filter ◽  
A Priori ◽  
Statistical Parameters ◽  
Estimation Errors ◽  
Adaptive Estimator ◽  
Practical Applications ◽  
Localization And Mapping ◽  
Cubature Kalman Filter ◽  
Slam Algorithm ◽  
Noise Statistics

We need to predict mathematical model of the system and a priori knowledge of the noise statistics when traditional simultaneous localization and mapping (SLAM) solutions are used. However, in many practical applications, prior statistics of the noise are unknown or time-varying, which will lead to large estimation errors or even cause divergence. In order to solve the above problem, an innovative cubature Kalman filter-based SLAM (CKF-SLAM) algorithm based on an adaptive cubature Kalman filter (ACKF) was established in this paper. The novel algorithm estimates the statistical parameters of the unknown system noise by introducing the Sage-Husa noise statistic estimator. Combining the advantages of the CKF-SLAM and the adaptive estimator, the new ACKF-SLAM algorithm can reduce the state estimated error significantly and improve the navigation accuracy of the SLAM system effectively. The performance of this new algorithm has been examined through numerical simulations in different scenarios. The results have shown that the position error can be effectively reduced with the new adaptive CKF-SLAM algorithm. Compared with other traditional SLAM methods, the accuracy of the nonlinear SLAM system is significantly improved. It verifies that the proposed ACKF-SLAM algorithm is valid and feasible.

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 A V-SLAM Guided and Portable System for Photogrammetric Applications

2021 ◽  
Vol 13 (12) ◽  
pp. 2351
Author(s):  
Alessandro Torresani ◽  
Fabio Menna ◽  
Roberto Battisti ◽  
Fabio Remondino
Keyword(s):  
Low Cost ◽  
Cost Effective ◽  
Motion Blur ◽  
Guidance System ◽  
Prototype System ◽  
Active Sensors ◽  
Localization And Mapping ◽  
Acquisition Speed ◽  
Mobile Mapping System ◽  
Slam Algorithm

Mobile and handheld mapping systems are becoming widely used nowadays as fast and cost-effective data acquisition systems for 3D reconstruction purposes. While most of the research and commercial systems are based on active sensors, solutions employing only cameras and photogrammetry are attracting more and more interest due to their significantly minor costs, size and power consumption. In this work we propose an ARM-based, low-cost and lightweight stereo vision mobile mapping system based on a Visual Simultaneous Localization And Mapping (V-SLAM) algorithm. The prototype system, named GuPho (Guided Photogrammetric System) also integrates an in-house guidance system which enables optimized image acquisitions, robust management of the cameras and feedback on positioning and acquisition speed. The presented results show the effectiveness of the developed prototype in mapping large scenarios, enabling motion blur prevention, robust camera exposure control and achieving accurate 3D results.

scholarly journals A decentralized Polynomial based SLAM algorithm for a team of mobile robots

2014 ◽  
Vol 47 (3) ◽  
pp. 10182-10187
Author(s):  
Luigi D'Alfonso ◽  
Pietro Muraca ◽  
Paolo Pugliese ◽  
Antonio Grano
Keyword(s):  
Mobile Robots ◽  
Slam Algorithm
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