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Open AccessArticle

3D Registration and Integrated Segmentation Framework for Heterogeneous Unmanned Robotic Systems

1
Department of Mechanics, Royal Military Academy, 1000 Brussels, Belgium
2
Department of Automatic Control and Electronics, Faculty of Electrical Engineering, 71000 Sarajevo, Bosnia and Herzegovina
3
ITS Research and Technology, AVL List GmbH, 8010 Graz, Austria
4
Department of Electrical Engineering and Information Technology, University of Naples Federico II, 80125 Napoli, Italy
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(10), 1608; https://doi.org/10.3390/rs12101608
Received: 13 March 2020 / Revised: 3 May 2020 / Accepted: 8 May 2020 / Published: 18 May 2020
(This article belongs to the Special Issue Advances and Innovative Applications of Unmanned Aerial Vehicles)
The paper proposes a novel framework for registering and segmenting 3D point clouds of large-scale natural terrain and complex environments coming from a multisensor heterogeneous robotics system, consisting of unmanned aerial and ground vehicles. This framework involves data acquisition and pre-processing, 3D heterogeneous registration and integrated multi-sensor based segmentation modules. The first module provides robust and accurate homogeneous registrations of 3D environmental models based on sensors’ measurements acquired from the ground (UGV) and aerial (UAV) robots. For 3D UGV registration, we proposed a novel local minima escape ICP (LME-ICP) method, which is based on the well known iterative closest point (ICP) algorithm extending it by the introduction of our local minima estimation and local minima escape mechanisms. It did not require any prior known pose estimation information acquired from sensing systems like odometry, global positioning system (GPS), or inertial measurement units (IMU). The 3D UAV registration has been performed using the Structure from Motion (SfM) approach. In order to improve and speed up the process of outliers removal for large-scale outdoor environments, we introduced the Fast Cluster Statistical Outlier Removal (FCSOR) method. This method was used to filter out the noise and to downsample the input data, which will spare computational and memory resources for further processing steps. Then, we co-registered a point cloud acquired from a laser ranger (UGV) and a point cloud generated from images (UAV) generated by the SfM method. The 3D heterogeneous module consists of a semi-automated 3D scan registration system, developed with the aim to overcome the shortcomings of the existing fully automated 3D registration approaches. This semi-automated registration system is based on the novel Scale Invariant Registration Method (SIRM). The SIRM provides the initial scaling between two heterogenous point clouds and provides an adaptive mechanism for tuning the mean scale, based on the difference between two consecutive estimated point clouds’ alignment error values. Once aligned, the resulting homogeneous ground-aerial point cloud is further processed by a segmentation module. For this purpose, we have proposed a system for integrated multi-sensor based segmentation of 3D point clouds. This system followed a two steps sequence: ground-object segmentation and color-based region-growing segmentation. The experimental validation of the proposed 3D heterogeneous registration and integrated segmentation framework was performed on large-scale datasets representing unstructured outdoor environments, demonstrating the potential and benefits of the proposed semi-automated 3D registration system in real-world environments. View Full-Text
Keywords: unmanned vehicles; outlier removal; 3D point cloud processing; large-scale environment; LME-ICP method; FCSOR; semi-automated registration; SIRM; multi-sensor based segmentation unmanned vehicles; outlier removal; 3D point cloud processing; large-scale environment; LME-ICP method; FCSOR; semi-automated registration; SIRM; multi-sensor based segmentation
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MDPI and ACS Style

Balta, H.; Velagic, J.; Beglerovic, H.; De Cubber, G.; Siciliano, B. 3D Registration and Integrated Segmentation Framework for Heterogeneous Unmanned Robotic Systems. Remote Sens. 2020, 12, 1608.

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