VinySLAM: An indoor SLAM method for low-cost platforms based on the Transferable Belief Model

Huletski, Arthur; Kartashov, Dmitriy; Krinkin, Kirill

doi:10.1109/iros.2017.8206595

Cited by 14 publications

(6 citation statements)

References 18 publications

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“…Mean scan processing time (MSPT) of the considered algorithms was tested on laptop (Core i7 2.6 GHz CPU, 8 GB RAM). The results of testing was 2.95 ms which corresponds to 19.93% of the MSPT of the [4], cited in the literature review.…”

Section: Resultsmentioning

confidence: 99%

“…The vinySLAM method [4] is used for robots that provide information about proximity to nearby obstacles with a laser scanner and it is supposed to be used in an indoor environment. The algorithm is proposed based on the MonteCarlo scan matcher and the random walk approach.…”

Section: Slam With Sparse and Noisy Sensorsmentioning

confidence: 99%

“…Even though SLAM is a popular theme, there is not much research focusing on low cost, sparse, noisy sensors, with measurement errors and sensor failures, such as ultrasonic sensors [3,4,5]. By using low cost sensors instead of those based on high-precision laser measurements, SLAM turns into an even more challenging task [6].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Principal Component Analysis (PCA) and Hough Transform as Tool for Simultaneous Localization and Mapping (SLAM) with Sparse and Noisy Sensors

Sousa

Carvalho

Freire

et al. 2020

JICS

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This work proposes a method of handling the difficulties generated by sparse and noisy sensorial output from a small quantity of ultrasonic sensors in order to develop a low cost SLAM system. A pre processing step of detecting faulty sensors was implemented by applying PCA on the available data in order to extract more reliable baseline features through the Hough Transform. Furthermore, we analyze the influence of odometry errors and failures in the localization of a differential driven mobile robot. This method is suitable for indoor and orthogonal shaped environments, especially for medium and short term tasks, such as exploration, rescue and inspection. The experimental results demonstrate the accuracy and robustness to noise and sensorial failures.

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Section: Resultsmentioning

confidence: 99%

Section: Slam With Sparse and Noisy Sensorsmentioning

confidence: 99%

See 1 more Smart Citation

Principal Component Analysis (PCA) and Hough Transform as Tool for Simultaneous Localization and Mapping (SLAM) with Sparse and Noisy Sensors

Sousa

Carvalho

Freire

et al. 2020

JICS

View full text Add to dashboard Cite

show abstract

“…For testing the scan filter was included in the operation of two SLAM algorithms: vinySLAM [18] and Google Cartographer [19]. The filter determines whether the scan should be processed or discarded before it is passed to the scan matcher of each of the listed algorithms.…”

Section: Evaluation Of the Quality And Accuracy Of The Laser Scan Filtermentioning

confidence: 99%

Scalable Algorithms for Simultaneous Mapping and Localization of Mobile Robot Swarms

Filatov¹,

Krinkin²

2023

Autonomous Mobile Mapping Robots

Self Cite

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The chapter is devoted to the development of scalable algorithms for multi-agent solution of the SLAM problem. These algorithms are applicable to robots with limited computational resources, having limited computational power and memory, small spatial size, and power from a portable battery. To simplify the description, only robots equipped with LIDAR are considered. The main focus is as follows: a scalable multi-agent SLAM algorithm based on Dempster-Shafer theory; an algorithm for filtering two-dimensional laser scans to free up computational resources; evaluation of the accuracy of the map and trajectory constructed by the multi-agent algorithm; performance evaluation on resource-limited computing devices.

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“…M AP building is a key module for autonomous mobile robots since both localization and planning usually depend on the map information of the environment [1], [2]. One typical map representation is the grid map [3], [4], wherein the value of each cell represents the probability of being occupied by obstacles. However, the required memory of the grid map grows rapidly with the increase of the environmental size.…”

Section: Introductionmentioning

confidence: 99%

CAE-RLSM: Consistent and Efficient Redundant Line Segment Merging for Online Feature Map Building

Wen

Zhang

Gao

et al. 2020

IEEE Trans. Instrum. Meas.

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In order to obtain a compact line segment-based map representation for localization and planning of mobile robots, it is necessary to merge redundant line segments which physically represent the same part of the environment in different scans. In this work, a consistent and efficient redundant line segment merging approach (CAE-RLSM) is proposed for online feature map building. The proposed CAE-RLSM is composed of two newly proposed modules: one-to-many incremental line segment merging (OTM-ILSM) and multi-processing global map adjustment (MP-GMA). Different from state-of-the-art offline merging approaches, the proposed CAE-RLSM can achieve real-time mapping performance, which not only reduces the redundancy of incremental merging with high efficiency, but also solves the problem of global map adjustment after loop closing to guarantee global consistency. Furthermore, a new correlation-based evaluation metric is proposed for the quality evaluation of line segment maps. This evaluation metric does not require manual measurement of the environmental metric information, instead it makes full use of globally consistent laser scans obtained by simultaneous localization and mapping (SLAM) systems to compare the performance of different line segment-based mapping approaches in an objective and fair manner. Comparative experimental results with respect to a mean shift-based offline redundant line segment merging approach (MS-RLSM) and an offline version of one-to-one incremental line segment merging approach (O 2 TO-ILSM) on both public datasets and self-recorded dataset are presented to show the superior performance of CAE-RLSM in terms of efficiency and map quality in different scenarios.

show abstract

VinySLAM: An indoor SLAM method for low-cost platforms based on the Transferable Belief Model

Cited by 14 publications

References 18 publications

Principal Component Analysis (PCA) and Hough Transform as Tool for Simultaneous Localization and Mapping (SLAM) with Sparse and Noisy Sensors

Principal Component Analysis (PCA) and Hough Transform as Tool for Simultaneous Localization and Mapping (SLAM) with Sparse and Noisy Sensors

Scalable Algorithms for Simultaneous Mapping and Localization of Mobile Robot Swarms

CAE-RLSM: Consistent and Efficient Redundant Line Segment Merging for Online Feature Map Building

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