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Embedding slam algorithms: Has it come of age?

ROBOTICS AND AUTONOMOUS SYSTEMS 2018年 100卷 14-26页

作者： Abouzahir, Mohamed Elouardi, Abdeihafid Latif, Rachid Bouaziz, Samir Tajer, Abdelouahed Univ Paris Saclay Univ Paris Sud CNRS SATIEUMR 8029Digiteo LabsBAT 660 F-91405 Orsay France ENSA LISTI Lab Agadir Morocco ENSA Lab Elect Engn & Syst Control Marrakech Morocco

Development of Simultaneous Localization and Mapping (slam) systems in the era of autonomous navigation and the growing demand for autonomous robots have put into question how to reduce the computational complexity and make use of slam algorithms to operate in real time. Our work, aims to take advantage of low-power embedded architectures to implement slam algorithms. Precisely, we evaluate the promise held by the new modern low power architectures in accelerating the execution time of slam algorithms. Throughout this, we map and implement 4 well-known slam algorithms that find utility in very different robot applications and autonomous navigation, on different architectures based embedded systems. We present first a processing time evaluation of these algorithms on different CPU based architectures. Results demonstrate that Fastslam2.0 allows a compromise between the computation time and the consistency of the localization results. The algorithm has been modified to be adapted to large environments. It is then optimized for parallel implementations on GPU and FPGA. A comparative study of the resulting implementations is given. Our results show that an embedded FPGA based SoC architecture is an interesting alternative for a slam algorithm implementation using the hardware software co-design approach. Hence, the system meets performance requirements of a robot to operate in real-time constraints. (C) 2017 Elsevier B.V. All rights reserved.

关键词： slam algorithms Heterogeneous architectures GPU CPU FPGA Hardware-software codesign

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Understanding Why slam algorithms Fail in Modern Indoor Environments 32nd

Understanding Why SLAM Algorithms Fail in Modern Indoor Envi...

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31st International Conference on Robotics in Alpe-Adria-Danube Region (RAAD)

作者： Nwankwo, Linus Rueckert, Elmar Montanuniv Chair Cyber Phys Syst Leoben Austria

ISBN: (纸本)9783031326080;9783031326066;9783031326059

Simultaneous localization and mapping (slam) algorithms are essential for the autonomous navigation of mobile robots. With the increasing demand for autonomous systems, it is crucial to evaluate and compare the performance of these algorithms in real-world environments. In this paper, we provide an evaluation strategy and real-world datasets to test and evaluate slam algorithms in complex and challenging indoor environments. Further, we analysed state-of-the-art (SOTA) slam algorithms based on various metrics such as absolute trajectory error, scale drift, and map accuracy and consistency. Our results demonstrate that SOTA slam algorithms often fail in challenging environments, with dynamic objects, transparent and reflecting surfaces. We also found that successful loop closures had a significant impact on the algorithm's performance. These findings highlight the need for further research to improve the robustness of the algorithms in real-world scenarios.

关键词： slam algorithms Mapping slam evaluation

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Self and Target Locating With Cooperation of Heterogeneous Unmanned Vehicles in the Denial Environment

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IEEE ACCESS 2025年 13卷 64699-64718页

作者： Amjad, Muhammad Ali, Md Sahin Yao, Shouwen Rahaman, Md Faishal Zheng, Changsong Kazim, Raza Muhammad Zouaoui, Bilal Beijing Inst Technol Sch Mech Engn Beijing 100081 Peoples R China Yangzhou Univ Sch Mech Engn Yangzhou 225127 Jiangsu Peoples R China Beijing Inst Technol Sch Comp Sci & Technol Beijing 100081 Peoples R China

The growing reliance on unmanned vehicles, such as drones and autonomous vehicles, has revolutionized both military and civilian applications, particularly in challenging environments where traditional reconnaissance methods fail. These systems are essential for tasks such as self-localization and target localization, particularly in denial environments where GPS and communication networks are compromised. However, effective cooperation of heterogeneous unmanned systems under these conditions remains a significant challenge. Without external positioning systems, ensuring reliable navigation and mission execution requires innovative approaches to localization, mapping, and path planning. This paper proposes an integrated system that enhances the cooperation between ground robots and aerial drones for self-localization and target localization in GPS-denied environments. The system incorporates advanced techniques such as the Extended Kalman Filter (EKF) for localization, G-mapping for environment mapping, Dijkstra's algorithm for global path planning, and the Dynamic Window Approach (DWA) for real-time local path replanning. Through simulations involving Husky robots and Hector quadcopters, the system demonstrates its ability to maintain accurate navigation and obstacle avoidance in communication-limited environments. The results demonstrate that the proposed system can successfully enable autonomous vehicles to cooperate and perform tasks reliably under challenging conditions. Future work will focus on expanding the framework to support a wider range of unmanned vehicles, improving control algorithms, and testing the system in even more complex denial environments to ensure continued robustness and adaptability.

关键词： Location awareness Vehicle dynamics Real-time systems Laser radar Heuristic algorithms Accuracy Autonomous vehicles Simultaneous localization and mapping Path planning Synchronization Heterogeneous unmanned vehicles sensor fusion denial environment self and targeting locating slam algorithms

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Sparse extended information filters: Insights into sparsification

Sparse extended information filters: Insights into sparsific...

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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Eustice, R Walter, M Leonard, J Woods Hole Oceanog Inst Dept Appl Ocean Phys & Engn Woods Hole MA 02543 USA

ISBN: (纸本)0780389123

Recently, there have been a number of variant Simultaneous Localization and Mapping (slam) algorithms that have made substantial progress towards large-area scalability by parameterizing the slam posterior within the information (canonical/inverse covariance) form. Of these, probably the most well-known and popular approach is the Sparse Extended Information Filter (SEIF) by Thrun et al. While SEIFs have been successfully implemented with a variety of challenging real-world datasets and have led to new insights into scalable slam, open research questions remain regarding the approximate sparsification procedure and its effect on map error consistency. In this paper, we examine the constant-time SEIF sparsification procedure in depth and offer new insight into issues of consistency. In particular, we show that exaggerated map inconsistency occurs within the global reference frame where estimation is performed, but that empirical testing shows that relative local map relationships are preserved. We then present a slightly modified version of their sparsification procedure, which is shown to preserve sparsity while also generating both local and global map estimates comparable to those obtained by the non-sparsified slam filter. While this modified approximation is no longer constant-time, it does serve as a theoretical benchmark against which to compare SEIFs constant-time results. We demonstrate our findings by benchmark comparison of the modified and original SEIF sparsification rule using simulation in the linear Gaussian slam case and real-world experiments for a nonlinear dataset.

关键词： intelligent robots position control slam algorithms linear Gaussian slam map error consistency simultaneous localization and mapping algorithms sparse extended information filters sparsification Intelligent robots Position control Web filters REFERENCE FRAMES maps simultaneous localization and mapping Modified

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Extended occupation grids for non-rigid moving objects tracking

Extended occupation grids for non-rigid moving objects track...

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14th International IEEE Conference on Intelligent Transportation Systems (ITSC)

作者： Lefaudeux, Benjamin Gate, Gwennael Nashashibi, Fawzi IMARA - INRIA Rocquencourt France Aldebaran Robotics France CAOR - Mines Paristech. France

ISBN: (纸本)9781457721977

We present an evolution of traditional occupancy grid algorithm, based on an extensive probabilistic calculus of the evolution of several variables on a cell neighbourhood. Occupancy, speed and classification are taken into account, the aim being to improve overall perception of an highly changing unstructured environment. Contrary to classical slam algorithms, no requisite is made on the amount of rigidity of the scene, and tracking do not rely on geometrical characteristics. We believe that this could have important applications in the automotive field, both from autonomous vehicle and driver assistance, in some areas difficult to address with current algorithms. This article begins with a general presentation of what we aim to do, along with considerations over traditional occupancy grids limits and their reasons. We will then present our proposition, and detail some of its key aspects, namely update rules and performance consequences. A second part will be more practical, and will begin with a brief presentation of the GPU implementation of the algorithm, before turning to sensor models and some results.

关键词： Approximation methods Bayesian methods Calculus Complexity theory GPU Graphics processing unit Laser radar Real time systems slam (robots) slam algorithms autonomous vehicle calculus driver assistance driver information systems extended occupation grids g

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Fast Artificial Landmark Detection for Indoor Mobile Robots

Fast Artificial Landmark Detection for Indoor Mobile Robots

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3rd International Conference on Innovative Network Systems and Applications (iNetSApp) held in conjunction with Federated Conference on Computer Science and Information Systems (FedCSIS)

作者： Kartashov, Dmitriy Huletski, Arthur Krinkin, Kirill Acad Univ St Petersburg Russia St Petersburg State Electrotech Univ LETI St Petersburg Russia

ISBN: (纸本)9788360810668

Nowadays the big challenge in simultaneous localization and mapping (slam) of mobile robots is the creation of efficient and robust algorithms. Significant Number of slam algorithms rely on unique features or or use artificial landmarks received from camera images. Feature points and landmarks extraction from images have two significant drawbacks: CPU consumption and weak robustness depending on environment conditions. In this paper we consider performance issues for landmark detection, introduce a new artificial landmark design and fast algorithm for detecting and tracking them in arbitrary images. Also we provide results of performance optimization for different hardware platforms.

关键词： slam (robots) cameras feature extraction mobile robots object detection robot vision slam algorithms arbitrary images artificial landmark detection camera images feature points hardware platforms indoor mobile robots landmark extraction performance issues performance optimization simultaneous localization and mapping Cameras Detection algorithms Graphics processing units Image color analysis Mobile robots Robustness

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Localization and Mapping algorithms Implemented on a Low-Power Embedded Architectures: A Case Study 5

Localization and Mapping Algorithms Implemented on a Low-Pow...

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5th International Conference on Multimedia Computing and Systems (ICMCS)

作者： Abouzahir, Mohamed Latif, Rachid Tajer, Abdelouahed Elouardi, Abdelhafid Bouaziz, Samir ENSA ESSI Agadir Morocco ENSA Marrakech Morocco Paris Saclay Univ Univ Paris Sud SATIE Digiteo LabsCNRSUMR 8029 BAT 660 F-91405 Orsay France

ISBN: (纸本)9781509051465

In this paper we evaluate the promise held by low-power embedded architectures to implement slam (Simultaneous Localization and Mapping) algorithms. We map and implement 4 slam algorithms, that find utility in very different robot applications and autonomous navigation, on an embedded architecture. Our results show that low-power embedded architectures are indeed, sometimes, attractive alternative for some slam algorithms. At the same time, efficient software optimizations are mandatory to allow a real-time execution.

关键词： Embedded Architectures slam algorithms Performances Evaluation

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Transferable Belief Models for Lightweight Simultaneous Localization and Mapping

Transferable Belief Models for Lightweight Simultaneous Loca...

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9th Mediterranean Conference on Embedded Computing (MECO)

作者： Krinkin, Kirin St Petersburg State Electrotech Univ LETI Dept Software Engn & Comp Applicat St Petersburg Russia

ISBN: (数字)9781728169491

ISBN: (纸本)9781728169491

Truly autonomous mobile robots have to solve the slam problem (i.e. simultaneous map building and pose estimation) in order to navigate in an unknown environment. This is a core problem to solve in order to reach real robot autonomy. Unfortunately, despite many solutions that exist, there is no robust and reliable solution for mobile platforms with limited resources. It's always a trade-off between robustness, performance, and computational resource requirements. In this talk, we discuss how to improve slam quality without increasing computation/memory limits, by using Dempster-Shafer probability and Transferable Belief Models. With the toy example (tinyslam) it will be shown how small enhancements can affect scan matching and occupancy tracking. Probability extensions could be one of the ways toward cheap smart spatial sensors.

关键词： Depster-Shafer Theory slam algorithms Transferable Beliefs Models Low Cost Mobile Platforms

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slam;definition and evolution

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2021年 97卷 104032-104032页

作者： Taheri, Hamid Xia, Zhao Chun Nanjing Univ Sci & Technol Comp Sci & Engn Dept Nanjing Peoples R China

Simultaneous Localization and Mapping (slam) is a key problem in the field of Artificial Intelligence and mobile robotics that addresses the problem of localization and mapping when a prior map of the workspace is not accessible. The determination of the slam problem has gained significant research momentum up to recent times. In this paper, firstly the problem of slam, its general model, framework, the difficulties, and leading approaches are described. Secondly, the progress of slam solving algorithms is surveyed throughout history. Pre-development, early slam solving algorithms, recent and present methods are presented and the progression of the state-of-art is reviewed based on the impact of leading approaches. We have selected some of the most important approaches of all time (1986-2019) to understand the research development, current trends, and intellectual structure of slam. Furthermore, from the trend of recent studies and the existence of difficult problems, a brief but sufficient review in the visual slam with the most outstanding approaches is presented. This paper provides one single sufficient review that allows researchers to understand the trend of slam, where it has come from, where it is going to and what needs to be more investigated in the slam-related field area. The future, in other words, the potential most important approaches inspiring the future researches in the slam problem can be seen. This paper will be an efficient overview and a valuable survey for introducing the slam solving approaches in mobile robotics as well as the general application of slam.

关键词： Intelligent robots Simultaneous localization and mapping slam algorithms slam history Intelligent agent

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