scholarly journals Metrological evaluation of laser scanner integrated with measuring arm using optical feature-based gauge

2019 ◽  
Vol 121 ◽  
pp. 120-132 ◽  
Author(s):  
E. Cuesta ◽  
B.J. Alvarez ◽  
S. Martinez-Pellitero ◽  
J. Barreiro ◽  
H. Patiño
Keyword(s):  
Laser Scanner ◽  
Optical Feature ◽  
Feature Based ◽  
Metrological Evaluation

scholarly journals Metrological evaluation of Structured Light 3D scanning system with an optical feature-based gauge

2017 ◽  
Vol 13 ◽  
pp. 526-533 ◽  
Author(s):  
E. Cuesta ◽  
J.M. Suarez-Mendez ◽  
S. Martinez-Pellitero ◽  
J. Barreiro ◽  
B.J. Alvarez ◽  
...  
Keyword(s):  
Structured Light ◽  
3D Scanning ◽  
Scanning System ◽  
Optical Feature ◽  
Feature Based ◽  
Metrological Evaluation

scholarly journals Localization in Unstructured Environments: Towards Autonomous Robots in Forests with Delaunay Triangulation

2020 ◽  
Vol 12 (11) ◽  
pp. 1870 ◽  
Author(s):  
Qingqing Li ◽  
Paavo Nevalainen ◽  
Jorge Peña Queralta ◽  
Jukka Heikkonen ◽  
Tomi Westerlund
Keyword(s):  
Autonomous Robots ◽  
Laser Scanner ◽  
Point Clouds ◽  
Time Data ◽  
Unstructured Environments ◽  
Accurate Localization ◽  
Novel Approach ◽  
Local Point ◽  
Feature Based ◽  
Real Time Data Processing

Autonomous harvesting and transportation is a long-term goal of the forest industry. One of the main challenges is the accurate localization of both vehicles and trees in a forest. Forests are unstructured environments where it is difficult to find a group of significant landmarks for current fast feature-based place recognition algorithms. This paper proposes a novel approach where local point clouds are matched to a global tree map using the Delaunay triangularization as the representation format. Instead of point cloud based matching methods, we utilize a topology-based method. First, tree trunk positions are registered at a prior run done by a forest harvester. Second, the resulting map is Delaunay triangularized. Third, a local submap of the autonomous robot is registered, triangularized and matched using triangular similarity maximization to estimate the position of the robot. We test our method on a dataset accumulated from a forestry site at Lieksa, Finland. A total length of 200 m of harvester path was recorded by an industrial harvester with a 3D laser scanner and a geolocation unit fixed to the frame. Our experiments show a 12 cm s.t.d. in the location accuracy and with real-time data processing for speeds not exceeding 0.5 m/s. The accuracy and speed limit are realistic during forest operations.

New procedure for qualification of structured light 3D scanners using an optical feature-based gauge

2018 ◽  
Vol 110 ◽  
pp. 193-206 ◽  
Author(s):  
Susana Martínez-Pellitero ◽  
Eduardo Cuesta ◽  
Sara Giganto ◽  
Joaquín Barreiro
Keyword(s):  
Structured Light ◽  
Optical Feature ◽  
3D Scanners ◽  
Feature Based

scholarly journals LINE SEGMENTATION OF 2D LASER SCANNER POINT CLOUDS FOR INDOOR SLAM BASED ON A RANGE OF RESIDUALS

2017 ◽  
Vol IV-2/W4 ◽  
pp. 363-369 ◽  
Author(s):  
M. Peter ◽  
S. R. U. N. Jafri ◽  
G. Vosselman
Keyword(s):  
Laser Scanning ◽  
Degrees Of Freedom ◽  
Large Scale ◽  
Laser Scanner ◽  
Point Clouds ◽  
Indoor Environments ◽  
Six Degrees Of Freedom ◽  
Localization And Mapping ◽  
Feature Based ◽  
Definition Of

Indoor mobile laser scanning (IMLS) based on the Simultaneous Localization and Mapping (SLAM) principle proves to be the preferred method to acquire data of indoor environments at a large scale. In previous work, we proposed a backpack IMLS system containing three 2D laser scanners and an according SLAM approach. The feature-based SLAM approach solves all six degrees of freedom simultaneously and builds on the association of lines to planes. Because of the iterative character of the SLAM process, the quality and reliability of the segmentation of linear segments in the scanlines plays a crucial role in the quality of the derived poses and consequently the point clouds. The orientations of the lines resulting from the segmentation can be influenced negatively by narrow objects which are nearly coplanar with walls (like e.g. doors) which will cause the line to be tilted if those objects are not detected as separate segments. State-of-the-art methods from the robotics domain like Iterative End Point Fit and Line Tracking were found to not handle such situations well. Thus, we describe a novel segmentation method based on the comparison of a range of residuals to a range of thresholds. For the definition of the thresholds we employ the fact that the expected value for the average of residuals of <i>n</i> points with respect to the line is <i>σ</i>&amp;thinsp;/&amp;thinsp;&amp;radic;<i>n</i>. Our method, as shown by the experiments and the comparison to other methods, is able to deliver more accurate results than the two approaches it was tested against.

Feature Based Mapping Procedure with Application on Simultaneous Localization and Mapping (SLAM)

2010 ◽  
Vol 166-167 ◽  
pp. 265-270
Author(s):  
Razvan Luca ◽  
Fritz Tröster ◽  
Robert Gall ◽  
Carmen Simion
Keyword(s):  
Autonomous Navigation ◽  
Autonomous Vehicle ◽  
Laser Scanner ◽  
Relevant Information ◽  
Process Data ◽  
Mapping Procedure ◽  
Localization And Mapping ◽  
Laser Scanners ◽  
Map Generation ◽  
Feature Based

We are presenting a feature based mapping procedure applied on data reduction to the relevant information used for autonomous navigation. The proceeding is based on the evaluation of the environment using a SICK LD laser scanner. We assume that laser scanners have the advantage of producing reliable data with well understood characteristics for map generation. By implementing evolutive algorithms we process data into lines representing edges of the surrounding objects and create a simplified representation of the environment (feature based). Because of the dynamic generation and evolution of the map, during the movement of the autonomous vehicle we are considering of merging and fitting the data by applying a shape correlation. The goal of our project defines the capability of a fully autonomous vehicle to safely drive through the environment until reaching the standard parking lots and complete autonomous parking procedures.

scholarly journals Semantic Mapping for Autonomous Subsea Intervention

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6740
Author(s):  
Guillem Vallicrosa ◽  
Khadidja Himri ◽  
Pere Ridao ◽  
Nuno Gracias
Keyword(s):  
Object Recognition ◽  
A Priori ◽  
Laser Scanner ◽  
Point Clouds ◽  
Bayesian Techniques ◽  
Semantic Map ◽  
3D Point Clouds ◽  
Feature Based ◽  
Object Classes ◽  
High Level

This paper presents a method to build a semantic map to assist an underwater vehicle-manipulator system in performing intervention tasks autonomously in a submerged man-made pipe structure. The method is based on the integration of feature-based slam and 3D object recognition using a database of a priori known objects. The robot uses dvl, pressure, and ahrs sensors for navigation and is equipped with a laser scanner providing non-coloured 3D point clouds of the inspected structure in real time. The object recognition module recognises the pipes and objects within the scan and passes them to the slam, which adds them to the map if not yet observed. Otherwise, it uses them to correct the map and the robot navigation if they were already mapped. The slam provides a consistent map and a drift-less navigation. Moreover, it provides a global identifier for every observed object instance and its pipe connectivity. This information is fed back to the object recognition module, where it is used to estimate the object classes using Bayesian techniques over the set of those object classes which are compatible in terms of pipe connectivity. This allows fusing of all the already available object observations to improve recognition. The outcome of the process is a semantic map made of pipes connected through valves, elbows and tees conforming to the real structure. Knowing the class and the position of objects will enable high-level manipulation commands in the near future.

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