A robust and hierarchical approach for the automatic co-registration of intensity and visible images

González‐Aguilera, Diego; Rodríguez‐Gonzálvez, Pablo; Hernández-López, David; Lerma, José Luis

doi:10.1016/j.optlastec.2012.01.034

Cited by 8 publications

(4 citation statements)

References 17 publications

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“…LIDAR intensity is also used to detect common features in multiple sets of remote sensing data for registration. Methods using LIDAR intensity data have been developed for segmentation of multiple scans [ 10 , 11 , 12 , 13 , 14 ] and co-registration of scans and images [ 15 , 16 , 17 , 18 , 19 , 20 ].…”

Section: Applications Of Lidar Intensitymentioning

confidence: 99%

A Review of LIDAR Radiometric Processing: From Ad Hoc Intensity Correction to Rigorous Radiometric Calibration

Kashani

Olsen

Parrish

et al. 2015

Sensors

283

214

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In addition to precise 3D coordinates, most light detection and ranging (LIDAR) systems also record “intensity”, loosely defined as the strength of the backscattered echo for each measured point. To date, LIDAR intensity data have proven beneficial in a wide range of applications because they are related to surface parameters, such as reflectance. While numerous procedures have been introduced in the scientific literature, and even commercial software, to enhance the utility of intensity data through a variety of “normalization”, “correction”, or “calibration” techniques, the current situation is complicated by a lack of standardization, as well as confusing, inconsistent use of terminology. In this paper, we first provide an overview of basic principles of LIDAR intensity measurements and applications utilizing intensity information from terrestrial, airborne topographic, and airborne bathymetric LIDAR. Next, we review effective parameters on intensity measurements, basic theory, and current intensity processing methods. We define terminology adopted from the most commonly-used conventions based on a review of current literature. Finally, we identify topics in need of further research. Ultimately, the presented information helps lay the foundation for future standards and specifications for LIDAR radiometric calibration.

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Section: Applications Of Lidar Intensitymentioning

confidence: 99%

A Review of LIDAR Radiometric Processing: From Ad Hoc Intensity Correction to Rigorous Radiometric Calibration

Kashani

Olsen

Parrish

et al. 2015

Sensors

283

214

View full text Add to dashboard Cite

show abstract

“…RANSAC was first published by Fischler and Bolles [ 43 ] in 1981 which is also often used in computer vision. For example, to simultaneously unravel correspondence problems such as fundamental matrices related to a pair of cameras, homograph estimation, motion estimation and image registration [ 44 , 45 , 46 , 47 , 48 , 49 ]. It is an iterative method to estimate parameters of a mathematical model from a set of observed data which contains outliers.…”

Section: Related Workmentioning

confidence: 99%

Pose Estimation of a Mobile Robot Based on Fusion of IMU Data and Vision Data Using an Extended Kalman Filter

Alatise

Hancke

2017

Sensors

167

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Using a single sensor to determine the pose estimation of a device cannot give accurate results. This paper presents a fusion of an inertial sensor of six degrees of freedom (6-DoF) which comprises the 3-axis of an accelerometer and the 3-axis of a gyroscope, and a vision to determine a low-cost and accurate position for an autonomous mobile robot. For vision, a monocular vision-based object detection algorithm speeded-up robust feature (SURF) and random sample consensus (RANSAC) algorithms were integrated and used to recognize a sample object in several images taken. As against the conventional method that depend on point-tracking, RANSAC uses an iterative method to estimate the parameters of a mathematical model from a set of captured data which contains outliers. With SURF and RANSAC, improved accuracy is certain; this is because of their ability to find interest points (features) under different viewing conditions using a Hessain matrix. This approach is proposed because of its simple implementation, low cost, and improved accuracy. With an extended Kalman filter (EKF), data from inertial sensors and a camera were fused to estimate the position and orientation of the mobile robot. All these sensors were mounted on the mobile robot to obtain an accurate localization. An indoor experiment was carried out to validate and evaluate the performance. Experimental results show that the proposed method is fast in computation, reliable and robust, and can be considered for practical applications. The performance of the experiments was verified by the ground truth data and root mean square errors (RMSEs).

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“…RANSAC was first published by Fischler and Bolles [85] in 1981 which is also often used in computer vision. It simultaneously unravel the correspondence problem such as, fundamental matrix related to a pair of cameras, homograph estimation, motion estimation and image registration [86]- [91]. It is an iterative method to estimate parameters of a mathematical model from a set of observed data which contains outliers.…”

Section: B Random Sample Consensus (Ransac)mentioning

confidence: 99%

A Review on Challenges of Autonomous Mobile Robot and Sensor Fusion Methods

2020

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Autonomous mobile robots are becoming more prominent in recent time because of their relevance and applications to the world today. Their ability to navigate in an environment without a need for physical or electro-mechanical guidance devices has made it more promising and useful. The use of autonomous mobile robots is emerging in different sectors such as companies, industries, hospital, institutions, agriculture and homes to improve services and daily activities. Due to technology advancement, the demand for mobile robot has increased due to the task they perform and services they render such as carrying heavy objects, monitoring, search and rescue missions, etc. Various studies have been carried out by researchers on the importance of mobile robot, its applications and challenges. This survey paper unravels the current literatures, the challenges mobile robot is being faced with. A comprehensive study on devices/sensors and prevalent sensor fusion techniques developed for tackling issues like localization, estimation and navigation in mobile robot are presented as well in which they are organised according to relevance, strengths and weaknesses. The study therefore gives good direction for further investigation on developing methods to deal with the discrepancies faced with autonomous mobile robot.

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A robust and hierarchical approach for the automatic co-registration of intensity and visible images

Cited by 8 publications

References 17 publications

A Review of LIDAR Radiometric Processing: From Ad Hoc Intensity Correction to Rigorous Radiometric Calibration

A Review of LIDAR Radiometric Processing: From Ad Hoc Intensity Correction to Rigorous Radiometric Calibration

Pose Estimation of a Mobile Robot Based on Fusion of IMU Data and Vision Data Using an Extended Kalman Filter

A Review on Challenges of Autonomous Mobile Robot and Sensor Fusion Methods

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