Online SLAM Based on a Fast Scan-Matching Algorithm

Pedrosa, Eurico; Lau, Nuno; Pereira, Artur

doi:10.1007/978-3-642-40669-0_26

Cited by 8 publications

(5 citation statements)

References 14 publications

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“…Having this in mind, we have tested 3 datasets typically used in SLAM benchmarking: ACES Building, Intel Research Lab, and Killian Court. 5 These were tested in the two computer architectures depicted in Table I The first immediate evidence is that scan matching takes less time to process in the Odroid X2 than in the Asus EeePC, as expected due to the former's superior computation power. Also, it is clear that multithreading accelerates the scan matching process by an approximate factor of 2.…”

Section: Resultsmentioning

confidence: 99%

“…These methods rely heavily on scan matching of consecutive sensor readings, with combination of other techniques like multi-resolution occupancy grid maps [4] or dynamic likelihood field models for measurement [5]. Despite the evident advancements in research in the SLAM problem, a robot with such capabilities still has to be equipped with a modern computer to adequately handle the processing and memory requirements.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Speeding up rao-blackwellized particle filter SLAM with a multithreaded architecture

Gouveia

Portugal

Marques

2014

2014 IEEE/RSJ International Conference on Intelligent Robots and Systems

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In this work we explore multiprocessor computer architectures to propose an effective method for solving the Simultaneous Localization and Mapping Problem. The proposed method makes use of multithreading to parallelize a Rao-Blackwellized Particle Filter approach. By applying the method in common computers found in robots, it is shown that a significant gain in efficiency can be obtained. Furthermore, the parallel method enables us to raise the number of particles up to values that would not be possible in a single threaded solution, thus gaining in localization precision and map accuracy. In order to analyze SLAM results, frequently used datasets by the robotics community were used, and a benchmarking metric was applied.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Speeding up rao-blackwellized particle filter SLAM with a multithreaded architecture

Gouveia

Portugal

Marques

2014

2014 IEEE/RSJ International Conference on Intelligent Robots and Systems

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“…These methods rely heavily on scan matching of consecutive sensor readings, with combination of other techniques, like multi-resolution occupancy grid maps [13], or dynamic likelihood field models for measurement [14]. Despite the evident advances in research on SLAM, most approaches do not consider environments disturbed by smoke, dust, or steam.…”

Section: Related Workmentioning

confidence: 99%

“…thresholds will vary. If the value of sonar readings do not match measurements taken by laser scans, and the smoke levels detected by the alcohol sensor hits values below a minimum threshold SM OKE M IN (130mV ) 14 , a greater confidence is given to LRF readings. If the alcohol sensor retrieves values greater than SM OKE M IN , sonar readings are superimposed to LRF readings.…”

Section: Smokenav V1 : Heuristic Modelmentioning

confidence: 99%

A Sensor Fusion Layer to Cope with Reduced Visibility in SLAM

Santos

Couceiro

Portugal

et al. 2015

J Intell Robot Syst

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Mapping and navigating with mobile robots in scenarios with reduced visibility, e.g. due to smoke, dust, or fog, is still a big challenge nowadays. In spite of the tremendous advance on Simultaneous Localization and Mapping (SLAM) techniques for the past decade, most of current algorithms fail in those environments because they usually rely on optical sensors providing dense range data, e.g. laser range finders, stereo vision, LIDARs, RGB-D, etc., whose measurement process is highly disturbed by particles of smoke, dust, or steam. This article addresses the problem of performing SLAM under reduced visibility conditions by proposing a sensor fusion layer which takes advantage from complementary characteristics between a laser range finder (LRF) and an array of sonars. This sensor fusion layer is ultimately used with a state-of-the-art SLAM technique to be resilient in scenarios where visibility cannot be assumed at all times. Special attention is given to mapping using commercial off-the-shelf (COTS) sensors, namely arrays of sonars which, being usually available in robotic platforms, raise technical issues that were investigated in the course of this work. Two sensor fusion methods, a heuristic method and a fuzzy logic-based method, are presented and discussed, corresponding to different stages of the research work conducted. The experimental validation of both methods with two different mobile robot platforms in smoky indoor scenarios showed that they provide a robust solution, using only COTS sensors, for adequately coping with reduced visibility in the SLAM process, thus decreasing significantly its impact in the mapping and localization results obtained.

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“…An advanced method likes sensor fusion in complementary characteristics of Laser Range Finder (LRF) that can map under reduced visibility conditions, e.g. particles of smoke [9]. In addition, classifying graph-based algorithms [10] [11] which use a robust function that generalizes classification and discard irrelevant measurements also is an efficiency solution to maintain large-scale maps.…”

Section: Introductionmentioning

confidence: 99%

Implementation of Odometry with EKF in Hector SLAM Methods

Jiang¹

2018

AUSMT

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Abstract:Map building for plain spatial soundings, such as a long and straight corridor in simultaneous localization and mapping (SLAM) is a challenging problem because of lacks of distinguishable landmarks. Such an environment is highly possible to induce erroneous mapping results, such as alias problems. This paper presents a scan matching algorithm with odometer prediction using Extended Kalman Filter (EKF) and an optimal path planning based on regression subgoals. The scan matching process can relax the problems of local minima by means of an effective correction in the odometrical information. By iterating odometrical corrections in each step of running motion model, the matching result can be better than one only believes in individual information from scanning or odometry. Meanwhile, an optimal path planning utilizing an A * algorithm with a regression method is introduced to ensure a mobile robot be able to move elaborately around the corner and speed up along a straight line. Experiments in an indoor environment have been conducted to verify the effectiveness and validation of the proposed techniques.

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Online SLAM Based on a Fast Scan-Matching Algorithm

Cited by 8 publications

References 14 publications

Speeding up rao-blackwellized particle filter SLAM with a multithreaded architecture

Speeding up rao-blackwellized particle filter SLAM with a multithreaded architecture

A Sensor Fusion Layer to Cope with Reduced Visibility in SLAM

Implementation of Odometry with EKF in Hector SLAM Methods

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