Grid-Based Scan-to-Map Matching for Accurate Simultaneous Localization and Mapping: Theory and Preliminary Numerical Study

2013 ◽  
Author(s):  
Kunjin Ryu ◽  
Tomonari Furukawa ◽  
Stanislaw Antol ◽  
Gamini Dissanayake
Keyword(s):  
Numerical Study ◽  
Estimation Error ◽  
Simultaneous Localization And Mapping ◽  
Map Matching ◽  
Normal Distributions ◽  
Localization And Mapping ◽  
Matching Technique ◽  
Global Matching ◽  
Mapping Theory ◽  
Grid Based

This paper presents a grid-based scan-to-map matching technique for accurate simultaneous localization and mapping (SLAM). At every acquisition of a new scan, the proposed technique estimates the relative position from which the previous scan was taken, and further corrects its estimation error by matching the new scan to the globally defined map. In order to achieve best scan-to-map matching at each acquisition, the map to match is represented as a grid map with multiple normal distributions (NDs) in each cell. Additionally, the new scan is also represented by NDs, developing a novel ND-to-ND matching technique. The ND-to-ND matching technique has significant potential in the enhancement of the global matching as well as the computational efficiency. Experimental results first show that the proposed technique successfully matches new scans to the map generating very small position and orientation errors, and then demonstrates the effectiveness of the multi-ND representation in comparison to the single-ND representation.

scholarly journals Pedestrian Navigation System with Trinal-IMUs for Drastic Motions

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5570
Author(s):  
Yiming Ding ◽  
Zhi Xiong ◽  
Wanling Li ◽  
Zhiguo Cao ◽  
Zhengchun Wang
Keyword(s):  
Navigation System ◽  
Inertial Sensor ◽  
Estimation Error ◽  
Simultaneous Localization And Mapping ◽  
Step Length ◽  
Mapping Method ◽  
Zero Crossing ◽  
Pedestrian Navigation ◽  
Length Estimation ◽  
Localization And Mapping

The combination of biomechanics and inertial pedestrian navigation research provides a very promising approach for pedestrian positioning in environments where Global Positioning System (GPS) signal is unavailable. However, in practical applications such as fire rescue and indoor security, the inertial sensor-based pedestrian navigation system is facing various challenges, especially the step length estimation errors and heading drift in running and sprint. In this paper, a trinal-node, including two thigh-worn inertial measurement units (IMU) and one waist-worn IMU, based simultaneous localization and occupation grid mapping method is proposed. Specifically, the gait detection and segmentation are realized by the zero-crossing detection of the difference of thighs pitch angle. A piecewise function between the step length and the probability distribution of waist horizontal acceleration is established to achieve accurate step length estimation both in regular walking and drastic motions. In addition, the simultaneous localization and mapping method based on occupancy grids, which involves the historic trajectory to improve the pedestrian’s pose estimation is introduced. The experiments show that the proposed trinal-node pedestrian inertial odometer can identify and segment each gait cycle in the walking, running, and sprint. The average step length estimation error is no more than 3.58% of the total travel distance in the motion speed from 1.23 m/s to 3.92 m/s. In combination with the proposed simultaneous localization and mapping method based on the occupancy grid, the localization error is less than 5 m in a single-story building of 2643.2 m2.

A New Approach for Measuring Terrain Profiles

2013 ◽  
Author(s):  
Stanislaw Antol ◽  
Kunjin Ryu ◽  
Tomonari Furukawa
Keyword(s):  
Feature Matching ◽  
Global Coordinate System ◽  
Map Matching ◽  
New Approach ◽  
Led Lighting ◽  
Matching Technique ◽  
Potential Applicability ◽  
Maintenance And Inspection ◽  
Vehicle Movement ◽  
Grid Based

This paper presents a new approach for measuring terrain profiles. The proposed approach uses RGB-D sensors to measure terrain surface relative to the vehicle. Since the RGB-D sensor is an area scanner, a feature matching technique called the grid-based feature-to-map matching technique, developed by the authors, matches objects seen in consecutive images and constructs a terrain profile in a global coordinate system. The RGB-D sensor are mounted inside a box to shield natural light and provide consistent LED lighting. As it constructs a terrain profile by matching scanned data regardless of the vehicle movement, the proposed approach is not affected by the vehicle movement as much as conventional techniques and achieves high accuracy. Preliminary experimental work indicates that the proposed approach could measure terrain profiles in sub-centimeter accuracy of all dimensions. The potential applicability of the proposed approach to the maintenance and inspection of railroads has also been demonstrated.

Map-Based Navigation of an Autonomous Car Using Grid-Based Scan-to-Map Matching

2015 ◽  
Author(s):  
Tomonari Furukawa ◽  
Kuya Takami ◽  
Xianqiao Tong ◽  
Daniel Watman ◽  
Abbi Hamed ◽  
...  
Keyword(s):  
High Speed ◽  
Grid Cell ◽  
Autonomous Driving ◽  
Map Matching ◽  
Matching Method ◽  
Normal Distributions ◽  
Small Robot ◽  
Speed And Accuracy ◽  
Toyota Prius ◽  
Grid Based

This paper presents the map-based navigation of a car with autonomous capabilities using grid-based scan-to-map matching. The autonomous car used for demonstration is built based on Toyota Prius and can control the throttle, the brake and the steering by a computer. The proposed grid-based scan-to-map matching method represents a map with a finite number of grid cells, represents a scan and the map with scan points at each grid as normal distributions (NDs) and constructs a map by matching the scan NDs to the map NDs. The proposed method enables scan-based mapping at high speed while maintaining high accuracy. The representation of a grid cell of a map in terms of multiple NDs further enhances speed and accuracy. The accuracy analysis of the proposed method shows that a small robot with a wheel diameter of 8cm had yielded no loop closure error after the travel of 186m while the terminal position error by the GMapping was approximately 1m with the error growth of 1%. The application of the proposed method with the autonomous car has then demonstrated the ability of the proposed method for autonomous driving with varying and high speed and has also quantified the significance of speed for successful mapping in autonomous driving.

Topological simultaneous localization and mapping: a survey

Robotica ◽  
2013 ◽  
Vol 32 (5) ◽  
pp. 803-821 ◽  
Author(s):  
Jaime Boal ◽  
Álvaro Sánchez-Miralles ◽  
Álvaro Arranz
Keyword(s):  
Feature Detection ◽  
Simultaneous Localization And Mapping ◽  
Map Matching ◽  
Unstructured Environments ◽  
Localization And Mapping ◽  
Viable Solution ◽  
Introductory Overview

SUMMARYOne of the main challenges in robotics is navigating autonomously through large, unknown, and unstructured environments. Simultaneous localization and mapping (SLAM) is currently regarded as a viable solution for this problem. As the traditional metric approach to SLAM is experiencing computational difficulties when exploring large areas, increasing attention is being paid to topological SLAM, which is bound to provide sufficiently accurate location estimates, while being significantly less computationally demanding. This paper intends to provide an introductory overview of the most prominent techniques that have been applied to topological SLAM in terms of feature detection, map matching, and map fusion.

Improved particle fusing geometric relation between particles in FastSLAM

Robotica ◽  
2009 ◽  
Vol 27 (6) ◽  
pp. 853-859 ◽  
Author(s):  
Inkyu Kim ◽  
Nosan Kwak ◽  
Heoncheol Lee ◽  
Beomhee Lee
Keyword(s):  
Standard Deviation ◽  
Particle Filter ◽  
Computer Simulations ◽  
Estimation Error ◽  
Simultaneous Localization And Mapping ◽  
Estimation Errors ◽  
Localization And Mapping ◽  
Geometric Relation ◽  
Error Standard Deviation ◽  
Over Time

SUMMARYFastSLAM is a framework for simultaneous localization and mapping using a Rao-Blackwellized particle filter (RBPF). But, FastSLAM is known to degenerate over time due to the loss of particle diversity, mainly caused by the particle depletion problem in resampling phase. In this work, improved particle filter using geometric relation between particles is proposed to restrain particle depletion and to reduce estimation errors and error variances. It uses a KD tree (k-dimensional tree) to derive geometric relation among particles and filters particles with importance weight conditions for resampling. Compared to the original particle filter used in FastSLAM, this technique showed less estimation error with lower error standard deviation in computer simulations.

scholarly journals A survey: which features are required for dynamic visual simultaneous localization and mapping?

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Zewen Xu ◽  
Zheng Rong ◽  
Yihong Wu
Keyword(s):  
Simultaneous Localization And Mapping ◽  
Dynamic Environments ◽  
Visual Features ◽  
Advantages And Disadvantages ◽  
Intelligent Robots ◽  
Localization And Mapping ◽  
High Level ◽  
Static World ◽  
Robotic Applications ◽  
Significant Attention

AbstractIn recent years, simultaneous localization and mapping in dynamic environments (dynamic SLAM) has attracted significant attention from both academia and industry. Some pioneering work on this technique has expanded the potential of robotic applications. Compared to standard SLAM under the static world assumption, dynamic SLAM divides features into static and dynamic categories and leverages each type of feature properly. Therefore, dynamic SLAM can provide more robust localization for intelligent robots that operate in complex dynamic environments. Additionally, to meet the demands of some high-level tasks, dynamic SLAM can be integrated with multiple object tracking. This article presents a survey on dynamic SLAM from the perspective of feature choices. A discussion of the advantages and disadvantages of different visual features is provided in this article.

scholarly journals Research on Laser-Visual Fusion-based Simultaneous Localization and Mapping

2020 ◽  
Vol 1682 ◽  
pp. 012049
Author(s):  
Jianjie Zhenga ◽  
Haitao Zhang ◽  
Kai Tang ◽  
Weidi Kong
Keyword(s):  
Simultaneous Localization And Mapping ◽  
Localization And Mapping

scholarly journals Modelling Software Architecture for Visual Simultaneous Localization and Mapping

Automation ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 48-61
Author(s):  
Bhavyansh Mishra ◽  
Robert Griffin ◽  
Hakki Erhan Sevil
Keyword(s):  
System Integration ◽  
Ad Hoc ◽  
Simultaneous Localization And Mapping ◽  
Software Systems ◽  
Model Driven ◽  
Performance Improvements ◽  
Software Model ◽  
Localization And Mapping ◽  
Stereo Cameras ◽  
Core Components

Visual simultaneous localization and mapping (VSLAM) is an essential technique used in areas such as robotics and augmented reality for pose estimation and 3D mapping. Research on VSLAM using both monocular and stereo cameras has grown significantly over the last two decades. There is, therefore, a need for emphasis on a comprehensive review of the evolving architecture of such algorithms in the literature. Although VSLAM algorithm pipelines share similar mathematical backbones, their implementations are individualized and the ad hoc nature of the interfacing between different modules of VSLAM pipelines complicates code reuseability and maintenance. This paper presents a software model for core components of VSLAM implementations and interfaces that govern data flow between them while also attempting to preserve the elements that offer performance improvements over the evolution of VSLAM architectures. The framework presented in this paper employs principles from model-driven engineering (MDE), which are used extensively in the development of large and complicated software systems. The presented VSLAM framework will assist researchers in improving the performance of individual modules of VSLAM while not having to spend time on system integration of those modules into VSLAM pipelines.

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