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Towards Camera-LIDAR Fusion-Based Terrain Modelling for Planetary Surfaces: Review and Analysis

Overview of attention for article published in Sensors, November 2016
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Article details
Title
Towards Camera-LIDAR Fusion-Based Terrain Modelling for Planetary Surfaces: Review and Analysis
Published in
Sensors, November 2016
DOI 10.3390/s16111952
Pubmed ID
Authors
Abstract

In recent decades, terrain modelling and reconstruction techniques have increased research interest in precise short and long distance autonomous navigation, localisation and mapping within field robotics. One of the most challenging applications is in relation to autonomous planetary exploration using mobile robots. Rovers deployed to explore extraterrestrial surfaces are required to perceive and model the environment with little or no intervention from the ground station. Up to date, stereopsis represents the state-of-the art method and can achieve short-distance planetary surface modelling. However, future space missions will require scene reconstruction at greater distance, fidelity and feature complexity, potentially using other sensors like Light Detection And Ranging (LIDAR). LIDAR has been extensively exploited for target detection, identification, and depth estimation in terrestrial robotics, but is still under development to become a viable technology for space robotics. This paper will first review current methods for scene reconstruction and terrain modelling using cameras in planetary robotics and LIDARs in terrestrial robotics; then we will propose camera-LIDAR fusion as a feasible technique to overcome the limitations of either of these individual sensors for planetary exploration. A comprehensive analysis will be presented to demonstrate the advantages of camera-LIDAR fusion in terms of range, fidelity, accuracy and computation.

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Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 64 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
Unknown 64 100%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Master 12 19%
Student > Ph. D. Student 10 16%
Researcher 10 16%
Student > Bachelor 4 6%
Student > Doctoral Student 3 5%
Other 6 9%
Unknown 19 30%
Readers by discipline
Readers by discipline Count As %
Engineering 26 41%
Computer Science 9 14%
Earth and Planetary Sciences 3 5%
Environmental Science 1 2%
Physics and Astronomy 1 2%
Other 2 3%
Unknown 22 34%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 23 November 2016.
All research outputs
#30,638,963
of 33,834,589 outputs
Outputs from Sensors
#28,859
of 38,094 outputs
Outputs of similar age
#396,710
of 451,096 outputs
Outputs of similar age from Sensors
#409
of 478 outputs
Altmetric has tracked 33,834,589 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 38,094 research outputs from this source. They receive a mean Attention Score of 3.6. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 478 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.