A review of optimisation strategies used in simultaneous localisation and mapping (original) (raw)
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Simultaneous Localisation and Mapping (SLAM): Part I The Essential Algorithms
— This tutorial provides an introduction to Simultaneous Localisation and Mapping (SLAM) and the extensive research on SLAM that has been undertaken over the past decade. SLAM is the process by which a mobile robot can build a map of an environment and at the same time use this map to compute it's own location. The past decade has seen rapid and exciting progress in solving the SLAM problem together with many compelling implementations of SLAM methods. Part I of this tutorial (this paper), describes the probabilistic form of the SLAM problem, essential solution methods and significant implementations. Part II of this tutorial will be concerned with recent advances in computational methods and new formulations of the SLAM problem for large scale and complex environments.
Linear SLAM: Linearising the SLAM problems using submap joining
Automatica, 2019
The main contribution of this paper is a new submap joining based approach for solving large-scale Simultaneous Localization and Mapping (SLAM) problems. Each local submap is independently built using the local information through solving a small-scale SLAM; the joining of submaps mainly involves solving linear least squares and performing nonlinear coordinate transformations. Through approximating the local submap information as the state estimate and its corresponding information matrix, judiciously selecting the submap coordinate frames, and approximating the joining of a large number of submaps by joining only two maps at a time, either sequentially or in a more efficient Divide and Conquer manner, the nonlinear optimization process involved in most of the existing submap joining approaches is avoided. Thus the proposed submap joining algorithm does not require initial guess or iterations since linear least squares problems have closed-form solutions. The proposed Linear SLAM technique is applicable to feature-based SLAM, pose graph SLAM and D-SLAM, in both two and three dimensions, and does not require any assumption on the character of the covariance matrices. Simulations and experiments are performed to evaluate the proposed Linear SLAM algorithm. Results using publicly available datasets in 2D and 3D show that Linear SLAM produces results that are very close to the best solutions that can be obtained using full nonlinear optimization algorithm started from an accurate initial guess. The C/C++ and MATLAB source codes of Linear SLAM are available on OpenSLAM.
2008
This paper surveys the most recent published techniques in the field of Simultaneous Localization and Mapping (SLAM). In particular it is focused on the existing techniques available to speed up the process, with the purpose to handel large scale scenarios. The main research field we plan to investigate is the filtering algorithms as a way of reducing the amount of data. It seems that almost all the current approaches can not perform consistent maps for large areas, mainly due to the increase of the computational cost and due to the uncertainties that become prohibitive when the scenario becomes larger.
A Review: Simultaneous Localization and Mapping in Application to Autonomous Robot
2018
The important characteristic that could assist in autonomous navigation is the ability of a mobile robot to concurrently construct a map for an unknown environment and localize itself within the same environment. This computational problem is known as Simultaneous Localization and Mapping (SLAM). In literature, researchers have studied this approach extensively and have proposed a lot of improvement towards it. More so, we are experiencing a steady transition of this technology to industries. However, there are still setbacks limiting the full acceptance of this technology even though the research had been conducted over the last 30 years. Thus, to determine the problems facing SLAM, this paper conducted a review on various foundation and recent SLAM algorithms. Challenges and open issues alongside the research direction for this area were discussed. However, towards addressing the problem discussed, a novel SLAM technique will be proposed.
Simultaneous Localization and Mapping in Application to Autonomous Robot
2018 International Conference on Intelligent and Innovative Computing Applications (ICONIC), 2018
The important characteristic that could assist in autonomous navigation is the ability of a mobile robot to concurrently construct a map for an unknown environment and localize itself within the same environment. This computational problem is known as Simultaneous Localization and Mapping (SLAM). In literature, researchers have studied this approach extensively and have proposed a lot of improvement towards it. More so, we are experiencing a steady transition of this technology to industries. However, there are still setbacks limiting the full acceptance of this technology even though the research had been conducted over the last 30 years. Thus, to determine the problems facing SLAM, this paper conducted a review on various foundation and recent SLAM algorithms. Challenges and open issues alongside the research direction for this area were discussed. However, towards addressing the problem discussed, a novel SLAM technique will be proposed.
Transactions of the Institute of Measurement and Control, 2014
A technique for optimising the efficiency of sub-map method for large-scale simultaneous localisation and mapping (SLAM) is proposed. It optimises the benefits of the sub-map technique to improve the accuracy and consistency of an Extended Kalman Filter (EKF) based SLAM. Error models were developed and engaged to investigate some of the outstanding issues in employing sub-map technique in SLAM. Such issues include: the size (distance) of an optimal sub-map, the acceptable error effect caused by the process noise covariance on the predictions and estimations made within a sub-map, when to terminate an existing sub-map and start a new one and the magnitude of the process noise covariance that could produce such an effect. Numerical results obtained from the study and an error correcting process was engaged to optimise the accuracy and convergence of the Invariant Information Local Sub-map Filter previously proposed. Applying this technique to the EKF-based SLAM algorithm: (a) reduces the computational burden of maintaining the global map estimates and (b) simplifies transformation complexities and data association ambiguities usually experienced in fusing sub-maps together. A Monte-Carlo analysis of the system is presented as a means of demonstrating the consistency and efficacy of the proposed technique.
A Review: Simultaneous Localization and Mapping Algorithms
Simultaneous Localization and Mapping (SLAM) involves creating an environmental map based on sensor data, while concurrently keeping track of the robot’s current position. Efficient and accurate SLAM is crucial for any mobile robot to perform robust navigation. It is also the keystone for higher-level tasks such as path planning and autonomous navigation. The past two decades have seen rapid and exciting progress in solving the SLAM problem together with many compelling implementations of SLAM methods. In this paper, we will review the two common families of SLAM algorithms: Kalman filter with its variations and particle filters. This article complements other surveys in this field by reviewing the representative algorithms and the state-of-the-art in each family. It clearly identifies the inherent relationship between the state estimation via the KF versus PF techniques, all of which are derivations of Bayes rule.
New framework for Simultaneous Localization and Mapping: Multi map SLAM
International Conference on Robotics and Automation, 2008
The main contribution of this paper arises from the development of a new framework, which has its inspiration in the mechanics of human navigation, for solving the problem of Simultaneous Localization and Mapping (SLAM). The proposed framework has specific relevance to vision based SLAM, in particular, small baseline stereo vision based SLAM and addresses several key issues relevant to the particular sensor domain. Firstly, as observed in the authors' earlier work, the particular sensing device has a highly nonlinear observation model resulting in inconsistent state estimations when standard recursive estimators such as the Extended Kalman Filter (EKF) or the Unscented variants are used.
Evaluations of different Simultaneous Localization and Mapping (SLAM) algorithms
IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society, 2012
Simultaneous Localization and Mapping (SLAM) algorithms with multiple autonomous robots have received considerable attention in recent years. In general, SLAM algorithms use odometry information and measurements from exteroceptive sensors of robots. The accuracy of these measurements and the performance of the corresponding SLAM algorithm directly affect the overall success of the system. This paper presents comparative performance evaluations of three Simultaneous Localization and Mapping (SLAM) algorithms using Extended Kalman Filter (EKF), Compressed Extended Kalman Filter (CEKF) and Unscented Kalman Filter (UKF). Specifically, it focuses on their SLAM performances and processing time requirements. To show the effect of CPU power on the processing time of SLAM algorithms, two notebooks and a net book with different specifications have been used. Comparative simulation results show that processing time requirements are consistent with the computational complexities of SLAM algorithms. The results we obtained are consistent with the CPU power tests of independent organizations and show that higher processing power decreases processing time accordingly. The results also show that CEKF is more suitable for outdoor SLAM applications where there are a lot of natural and artificial features.
International Journal of Innovative Computing
To aid in robot navigation and environment analysis, visual SLAM systems process visual data. Things like AMRs (autonomous mobile robots) and AGVs (autonomous guided vehicles) have been gaining popularity in recent years. These robots depend significantly on simultaneous localization and mapping (SLAM) technology to keep the factory floor free of accidents. vSLAM employs a technique for estimating the precise positioning and orientation of a sensor relative to its environment as well as the navigation of the region around it. SLAM algorithms can be used in various applications, including self-driving vehicles, mobile robots, drones, etc. Visual SLAM does not refer to a particular set of methods or software. This paper proposes to review some of the issues and challenges facing SLAM technology in autonomous robot applications and draw a conclusion.