Lightweight RGB-D SLAM System for Search and Rescue Robots

Belter, Dominik; Nowicki, Michał; Skrzypczyński, Piotr; Walas, Krzysztof; Wietrzykowski, Jan

doi:10.1007/978-3-319-15847-1_2

Cited by 18 publications

(6 citation statements)

References 18 publications

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“…Similarly, the appearance of inexpensive RGB-D sensors [33,15], such as Microsoft Kinect released in 2010, triggered a massive wave of interest in 3D visual perception [28]. Access to the color images supplemented with depth maps provided by such sensors changed the the entire field of robotics, starting with their application to map building [26] and SLAM [36,3], through recognition of human posture [7] or hand gestures [27], ending on object grasping [6].…”

Section: Motivation Of the Workmentioning

confidence: 99%

Modreg: A Modular Framework for RGB-D Image Acquisition and 3D Object Model Registration

Kornuta

Stefańczyk

2017

Foundations of Computing and Decision Sciences

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Abstract. RGB-D sensors became a standard in robotic applications requiring object recognition, such as object grasping and manipulation. A typical object recognition system relies on matching of features extracted from RGB-D images retrieved from the robot sensors with the features of the object models. In this paper we present ModReg: a system for registration of 3D models of objects. The system consists of a modular software associated with a multi-camera setup supplemented with an additional pattern projector, used for the registration of high-resolution RGB-D images. The objects are placed on a fiducial board with two dot patterns enabling extraction of masks of the placed objects and estimation of their initial poses. The acquired dense point clouds constituting subsequent object views undergo pairwise registration and at the end are optimized with a graph-based technique derived from SLAM. The combination of all those elements resulted in a system able to generate consistent 3D models of objects.Keywords: ModReg, textured stereo, RGB-D image, point cloud, registration Motivation of the workEmergence of new types of sensors stimulates robotics. Up to the late nineties the most popular sensors used for obstacle avoidance were sonars, whereas the commercialization of laser rangefinders (such as the Hokuyo UTM and SICK LMS series) enabled rapid development of methods for 2D robot pose estimation [21] or techniques for simultaneous localization and map building (SLAM) [38]. Similarly, the appearance of inexpensive RGB-D sensors [33,15], such as Microsoft Kinect released

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Section: Motivation Of the Workmentioning

confidence: 99%

Modreg: A Modular Framework for RGB-D Image Acquisition and 3D Object Model Registration

Kornuta

Stefańczyk

2017

Foundations of Computing and Decision Sciences

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“…The PrimeSense-based sensor returns accurate depth data indoors. Because Kinect and Asus Xtion use near-infrared structured light projectors to obtain information about the distance to the objects, these sensors do not operate outdoors (Belter et al, 2015).…”

Section: Belter Et Almentioning

confidence: 99%

“…This shows that the proposed mapping method requires precise localization to work properly. For this purpose, parallel tracking and mapping method (Belter and Skrzypczyński, 2013) or RGB-D SLAM (Belter et al, 2015) can be used. Our experience shows that the localization error of these methods is below 2 cm for small workspaces with a sufficient number of visual features.…”

Section: 2mentioning

confidence: 99%

RGB–D terrain perception and dense mapping for legged robots

Belter

Łabęcki

Fankhauser

et al. 2016

International Journal of Applied Mathematics and Computer Science

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This paper addresses the issues of unstructured terrain modeling for the purpose of navigation with legged robots. We present an improved elevation grid concept adopted to the specific requirements of a small legged robot with limited perceptual capabilities. We propose an extension of the elevation grid update mechanism by incorporating a formal treatment of the spatial uncertainty. Moreover, this paper presents uncertainty models for a structured light RGB-D sensor and a stereo vision camera used to produce a dense depth map. The model for the uncertainty of the stereo vision camera is based on uncertainty propagation from calibration, through undistortion and rectification algorithms, allowing calculation of the uncertainty of measured 3D point coordinates. The proposed uncertainty models were used for the construction of a terrain elevation map using the Videre Design STOC stereo vision camera and Kinect-like range sensors. We provide experimental verification of the proposed mapping method, and a comparison with another recently published terrain mapping method for walking robots.

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“…Along the past few years, the use of mobile robots has extended to a wide range of applications thanks mainly to the improvement of their perception and processing abilities. Numerous examples can be found in the literature, such as in search and rescue applications [1], [2], social assistive robots [3], mobile manipulation [4], navigation in densely crowded environments [5] and material handling in manufacturing systems [6].…”

Section: Introductionmentioning

confidence: 99%

A Novel Method to Estimate the Position of a Mobile Robot in Underfloor Environments Using RGB-D Point Clouds

et al. 2020

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This work was supported by the Spanish government through the project DPI 2016-78361-R (AEI/FEDER, UE): ''Creación de mapas mediante métodos de apariencia visual para la navegación de robots''; by the Generalitat Valenciana through the project AICO/2019/031: ''Creación de modelos jerárquicos y localización robusta de robots móviles en entornos sociales'' and by the European Union's Horizon 2020 research and innovation program under grant agreement No. 698607.

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Lightweight RGB-D SLAM System for Search and Rescue Robots

Cited by 18 publications

References 18 publications

Modreg: A Modular Framework for RGB-D Image Acquisition and 3D Object Model Registration

Modreg: A Modular Framework for RGB-D Image Acquisition and 3D Object Model Registration

RGB–D terrain perception and dense mapping for legged robots

A Novel Method to Estimate the Position of a Mobile Robot in Underfloor Environments Using RGB-D Point Clouds

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