Sparse point cloud registration and aggregation with mesh‐based generalized iterative closest point

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Title

Sparse point cloud registration and aggregation with mesh‐based generalized iterative closest point

Content partner

University of Canterbury Library

Collection

UC Research Repository

Description

Accurate registration is critical for robotic mapping and simultaneous localization and mapping (SLAM). Sparse or non-uniform point clouds can be very challenging to register, even in ideal environments. Previous research by Holz et al. has developed a mesh-based extension to the popular generalized iterative closest point (GICP) algorithm, which can accurately register sparse clouds where unmodified GICP would fail. This paper builds on that work by expanding the comparison between the two a...

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Format

Research paper

Research format

Journal article

Date created

2021

Creator

Young M / McCulloch J / Green R / Pretty, Christopher

URL

https://hdl.handle.net/10092/102808

Related subjects

PCL / sparse point cloud / GICP / registration / Artificial Intelligence and Image Processing / Electrical and Electronic Engineering / Mechanical Engineering / Engineering / Control engineering, mechatronics and robotics / Automation engineering / Field robotics / Information and computing sciences / Applied computing / Spatial data and applications / Theory of computation / Data structures and algorithms

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You can learn more about the rights status of this item at: http://hdl.handle.net/10092/17651

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