An ICP variant using a point-to-line metric

Censi, Andrea

doi:10.1109/robot.2008.4543181

Cited by 528 publications

(336 citation statements)

References 11 publications

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“…Theoretically, the first method extracts features from range scans, such as lines, corners, and semicircles of tree trunks, and then matches the extracted known features with a generated feature map to recognize the position. In relative positioning, on the contrary, scan matching utilizes two or more consecutive frames of scan points to directly obtain the movement mobile platform with various algorithms, e.g., classical Iterative Closest Point (ICP) [23], Iterative Closest Line (ICL) [24], Monte Carlo [25], and Maximum Likelihood Estimation (MLE) [26,27]. SLAM is mainly utilized in robotics for various indoor applications.…”

Section: Slam Developed For Forestrymentioning

confidence: 99%

SLAM-Aided Stem Mapping for Forest Inventory with Small-Footprint Mobile LiDAR

Tang

Chen

Kukko

et al. 2015

Forests

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Accurately retrieving tree stem location distributions is a basic requirement for biomass estimation of forest inventory. Combining Inertial Measurement Units (IMU) with Global Navigation Satellite Systems (GNSS) is a commonly used positioning strategy in most Mobile Laser Scanning (MLS) systems for accurate forest mapping. Coupled with a tactical or consumer grade IMU, GNSS offers a satisfactory solution in open forest environments, for which positioning accuracy better than one decimeter can be achieved. However, for such MLS systems, positioning in a mature and dense forest is still a challenging task because of the loss of GNSS signals attenuated by thick canopy. Most often laser scanning sensors in MLS systems are used for mapping and modelling rather than positioning. In this paper, we investigate a Simultaneous Localization and Mapping (SLAM)-aided positioning solution with point clouds collected by a small-footprint LiDAR. Based on the field test data, we evaluate the potential of SLAM positioning and mapping in forest inventories. The results show that the positioning accuracy in the selected test field is improved by 38% compared to that of a traditional tactical grade IMU + GNSS positioning system in a mature forest environment and, as a result, we are able to produce a unambiguous tree distribution map.

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Section: Slam Developed For Forestrymentioning

confidence: 99%

SLAM-Aided Stem Mapping for Forest Inventory with Small-Footprint Mobile LiDAR

Tang

Chen

Kukko

et al. 2015

Forests

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“…Another important feature of our method is that it works with raw 3D data, it is said, unorganized 3D points sets. In contrast to other previous works on 6D mapping like Borrmann et al (2008); Kümmerle et al (2008); Censi (2008); Armesto et al (2010) this feature makes our method independent of the 3D sensor used to obtain the data. In this way, applying our method for different robots equipped with different sensors does not require a big programming effort.…”

Section: Resultsmentioning

confidence: 94%

“…In Salvi et al (2007) a deeper study comparing different solutions for ICP in terms of speed and accuracy is performed. Nowadays, more ICP variations are still appearing Du et al (2007a,b); Nuchter et al (2007);Censi (2008); Armesto et al (2010).…”

mentioning

confidence: 99%

A robust and fast method for 6DoF motion estimation from generalized 3D data

Viejo

Cazorla

2013

Auton Robot

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Nowadays, there is an increasing number of robotic applications that need to act in real three-dimensional (3D) scenarios. In this paper we present a new mobile robotics orientated 3D registration method that improves previous Iterative Closest Points based solutions both in speed and accuracy. As an initial step, we perform a low cost computational method to obtain descriptions for 3D scenes planar surfaces. Then, from these descriptions we apply a force system in order to compute accurately and efficiently a six Degrees of Freedom egomotion. We describe the basis of our approach and demonstrate its validity with several experiments using different kinds of 3D sensors and different 3D real environments.

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“…Lu and Milios (1997) present an application of ICP with the data obtained from a laser range finder. Following the same line of thought, the Iterative Closest Line (ICL) algorithm (Censi 2008) matches a set of points and a set of lines. The major disadvantage of these algorithms is the high computational effort for computing the correspondence search between the sets of points.…”

Section: Related Workmentioning

confidence: 99%

Robust mobile robot localization based on a security laser: an industry case study

Sobreira

Moreira

Costa

et al. 2016

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Purpose This paper aims to address a mobile robot localization system that avoids using a dedicated laser scanner, making it possible to reduce implementation costs and the robot’s size. The system has enough precision and robustness to meet the requirements of industrial environments. Design/methodology/approach Using an algorithm for artificial beacon detection combined with a Kalman Filter and an outlier rejection method, it was possible to enhance the precision and robustness of the overall localization system. Findings Usually, industrial automatic guide vehicles feature two kinds of lasers: one for navigation placed on top of the robot and another for obstacle detection (security lasers). Recently, security lasers extended their output data with obstacle distance (contours) and reflectivity. These new features made it possible to develop a novel localization system based on a security laser. Research limitations/implications Once the proposed methodology is completely validated, in the future, a scheme for global localization and failure detection should be addressed. Practical implications This paper presents a comparison between the presented approach and a commercial localization system for industry. The proposed algorithms were tested in an industrial application under realistic working conditions. Social implications The presented methodology represents a gain in the effective cost of the mobile robot platform, as it discards the need for a dedicated laser for localization purposes. Originality/value This paper presents a novel approach that benefits from the presence of a security laser on mobile robots (mandatory sensor when considering industrial applications), using it simultaneously with other sensors, not only to guarantee safety conditions during operation but also to locate the robot in the environment. This paper is also valuable because of the comparison made with a commercialized system, as well as the tests conducted in real industrial environments, which prove that the approach presented is suitable for working under these demanding conditions.

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An ICP variant using a point-to-line metric

Cited by 528 publications

References 11 publications

SLAM-Aided Stem Mapping for Forest Inventory with Small-Footprint Mobile LiDAR

SLAM-Aided Stem Mapping for Forest Inventory with Small-Footprint Mobile LiDAR

A robust and fast method for 6DoF motion estimation from generalized 3D data

Robust mobile robot localization based on a security laser: an industry case study

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