A scan matching method using Euclidean invariant signature for global localization and map building

2004 ◽  
Author(s):  
M. Tomono
Keyword(s):  
Map Building ◽  
Matching Method ◽  
Global Localization ◽  
Scan Matching

scholarly journals VECTOR MAPS IN MOBILE ROBOTICS

2015 ◽  
Vol 2 (2) ◽  
pp. 22-28 ◽  
Author(s):  
Ales Jelinek
Keyword(s):  
Data Acquisition ◽  
Navigation System ◽  
Mobile Robotics ◽  
Map Building ◽  
Scan Matching ◽  
Line Extraction ◽  
Current State ◽  
Vector Maps ◽  
Localization And Mapping ◽  
University Of Technology

The aim of this paper is to provide a brief overview of vector map techniques used in mobile robotics and to present current state of the research in this field at the Brno University of Technology. Vector maps are described as a part of the simultaneous localization and mapping (SLAM) problem in the environment without artificial landmarks or global navigation system. The paper describes algorithms from data acquisition to map building but particular emphasis is put on segmentation, line extraction and scan matching algorithms. All significant algorithms are illustrated with experimental results.

scholarly journals Polar Metric-Weighted Norm-Based Scan Matching for Robot Pose Estimation

2016 ◽  
Vol 2016 ◽  
pp. 1-20
Author(s):  
Guanglei Huo ◽  
Lijun Zhao ◽  
Ke Wang ◽  
Ruifeng Li ◽  
Jianqiang Li
Keyword(s):  
Pose Estimation ◽  
Value Function ◽  
Rotation Angle ◽  
Accurate Estimation ◽  
Weighted Norm ◽  
Map Building ◽  
Scan Matching ◽  
Constant Weight ◽  
Absolute Value ◽  
Point To Point

A novel point-to-point scan matching approach is proposed to address pose estimation and map building issues of mobile robots. Polar Scan Matching (PSM) and Metric-Based Iterative Closest Point (Mb-ICP) are usually employed for point-to-point scan matching tasks. However, due to the facts that PSM considers the distribution similarity of polar radii in irrelevant region of reference and current scans and Mb-ICP assumes a constant weight in the norm about rotation angle, they may lead to a mismatching of the reference and current scan in real-world scenarios. In order to obtain better match results and accurate estimation of the robot pose, we introduce a new metric rule, Polar Metric-Weighted Norm (PMWN), which takes both rotation and translation into account to match the reference and current scan. For robot pose estimation, the heading rotation angle is estimated by correspondences establishing results and further corrected by an absolute-value function, and then the geometric property of PMWN called projected circle is used to estimate the robot translation. The extensive experiments are conducted to evaluate the performance of PMWN-based approach. The results show that the proposed approach outperforms PSM and Mb-ICP in terms of accuracy, efficiency, and loop closure error of mapping.

scholarly journals An efficient likelihood field mapping method and high speed scan matching method using it

2017 ◽  
Vol 83 (853) ◽  
pp. 17-00016-17-00016
Author(s):  
Mitsuhiro MATSUNAGA ◽  
Kazuo NAKAZAWA
Keyword(s):  
High Speed ◽  
Mapping Method ◽  
Matching Method ◽  
Scan Matching ◽  
Field Mapping

scholarly journals Keyframes Global Map Establishing Method for Robot Localization through Content-Based Image Matching

2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Tianyang Cao ◽  
Haoyuan Cai ◽  
Dongming Fang ◽  
Hui Huang ◽  
Chang Liu
Keyword(s):  
Image Matching ◽  
Robot Localization ◽  
Map Building ◽  
Matching Method ◽  
Large Space ◽  
Localization And Mapping ◽  
Indoor Mobile Robot ◽  
Cleaning Robots ◽  
Improving Accuracy ◽  
Space Distance

Self-localization and mapping are important for indoor mobile robot. We report a robust algorithm for map building and subsequent localization especially suited for indoor floor-cleaning robots. Common methods, for example, SLAM, can easily be kidnapped by colliding or disturbed by similar objects. Therefore, keyframes global map establishing method for robot localization in multiple rooms and corridors is needed. Content-based image matching is the core of this method. It is designed for the situation, by establishing keyframes containing both floor and distorted wall images. Image distortion, caused by robot view angle and movement, is analyzed and deduced. And an image matching solution is presented, consisting of extraction of overlap regions of keyframes extraction and overlap region rebuild through subblocks matching. For improving accuracy, ceiling points detecting and mismatching subblocks checking methods are incorporated. This matching method can process environment video effectively. In experiments, less than 5% frames are extracted as keyframes to build global map, which have large space distance and overlap each other. Through this method, robot can localize itself by matching its real-time vision frames with our keyframes map. Even with many similar objects/background in the environment or kidnapping robot, robot localization is achieved with position RMSE <0.5 m.

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