Learning compact 3D models of indoor and outdoor environments with a mobile robot

By Dirk Hähnel, Wolfram Burgard, Sebastian Thrun. Robotics Auton. Syst., 2003

366 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/59ca31ae97c8ced33fd6badfdc2f3289d3be8c0c

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Citations in the corpus

Robot Programming by Demonstration (2010, background): “One popular alternative to visual learning systems is to reconstruct the geometry of the local environment from single or multiple views [11] or using 3D laser scanners [9,17].”
Multiple‐Robot Simultaneous Localization and Mapping: A Review (2016, methodology): “Polygon-based SLAM is used for 3D mapping (Hahnel et al., 2003b).”
Depth camera based indoor mobile robot localization and navigation (2012, methodology): “There has been considerable work in 2D localization and mapping ([5] provides an overview of the recent advances in SLAM), and in using localization on 2D maps to generate 3D models using additional scans [6].”
A clustering method for efficient segmentation of 3D laser data (2008, background): “a SICK LMS, in some vertical orientation at a fixed position on the robot [1], [2].”
Trajectory Optimization using Reinforcement Learning for Map Exploration (2008, background): “Further, these innovations have led to robots that can map large-scale, outdoor environments with a variety of different sensors and to the creation of commodity open-source community around mapping software (Haehnel et al. 2003; Thrun and Montemerlo 2005; Martinez-Cantin and Castellanos 2005; Marti”
Multi-Level Surface Maps for Outdoor Terrain Mapping and Loop Closing (2006, methodology): “[7] use a region growing technique to identify planes.”
An autonomous mobile robot with a 3D laser range finder for 3D exploration and digitalization of indoor environments (2003, unknown): “Thrun et al. [1] [2], Früh and Zakhor [3] and Zhao and Shibasaki [4] use two 2D laser range finder for acquiring 3D data.”
Automatic Creation of Semantically Rich 3D Building Models from Laser Scanner Data (2011, methodology): “Thrun et al. developed a plane extraction method based on the expectation-maximization algorithm (Thrun et al., 2004), and Hahnel et al. used a plane sweep approach to ﬁnd planar regions (H¨ahnel et al., 2003).”
State of research in automatic as-built modelling (2015, background): “In the same line of works, many versions of region growing algorithms have been proposed to extract planar patches from point clouds, by first clustering the points using some local measurements, and then fitting planes on the resulted clusters [57,86,61].”