Towards 3D Point cloud based object maps for household environments

By Radu Bogdan Rusu, Zoltán-Csaba Márton, Nico Blodow, Mihai Emanuel Dolha, Michael Beetz. 2008

1,170 citations recorded by the source corpus.

Subfields: Computational Mechanics, Environmental Engineering, Geology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

In Environmental Engineering

In Geology

Citations in the corpus

Dynamic Graph CNN for Learning on Point Clouds (2018, neutral): “A few of the many recent applications of point cloud processing and analysis include indoor navigation [57], self-driving vehicles [33], robotics [40], and shape synthesis and modeling [14].”
Close-range scene segmentation and reconstruction of 3D point cloud maps for mobile manipulation in domestic environments (2009, methodology): “Our method estimates sets of hybrid geometric object models that consist of shape as well as surface models.”
Contextually guided semantic labeling and search for three-dimensional point clouds (2013, background): “Some earlier works (Dima et al., 2004; Gould et al., 2008; Rusu et al., 2008; Quigley et al., 2009) have tried to combine shape and color information from multiple sensors for tasks such as object and obstacle detection.”
Semantic mapping for mobile robotics tasks: A survey (2015, background): “Accordingly, the works described in [37], [38], [39], [40] and [41] employ laser scanners mounted on mobile robots to reconstruct the 3D environment.”
Push planning for object placement on cluttered table surfaces (2011, methodology): “Section VI places our planner in the broader context of clutter manipulation.”
PAConv: Position Adaptive Convolution with Dynamic Kernel Assembling on Point Clouds (2021, background): “, autonomous driving, robotic manipulation and virtual reality [35, 40].”
KnowRob: A knowledge processing infrastructure for cognition-enabled robots (2013, background): “Recent advances in object recognition however lead to more research on semantic maps and thereby to a wider use of higher-level semantic knowledge in robotic applications, although the “level of semantics” ranges from a mere classification into different parts and object types (Rusu et al., 2008) or”
Automatic Reconstruction of As-Built Building Information Models from Laser-Scanned Point Clouds: A Review of Related Techniques | NIST (2010, background): “who use heuristics to detect walls, floors, ceilings, and cabinets in a kitchen environment [81].”
A Multi-view Stereo Benchmark with High-Resolution Images and Multi-camera Videos (2017, methodology): “Therefore, we filter the scans with the statistical outlier removal procedure from [31].”
KNOWROB — knowledge processing for autonomous personal robots (2009, methodology): “During the mapping process, the point clouds created by the laser scanner are segmented into single objects, which are then classified into categories like “Cupboard” or “Dishwasher” as described in [8].”
Real-time compression of point cloud streams (2012, background): “This sensor data can be efficiently represented by point clouds from which a robot can infer information about the shape and geometry of their environment, detect objects as well as to orient itself [1].”
RGCNN: Regularized Graph CNN for Point Cloud Segmentation (2018, methodology): “Modeldriven methods include edge-based [21], region-growing [22] and model-fitting [27], which are based on the prior knowledge of the geometry but sensitive to noise, uneven density and complicated structure.”
Fast Point Feature Histograms (FPFH) for 3D registration (2009, background): “As we previously proposed in [1], [16], Point Feature His-”
Automatic Creation of Semantically Rich 3D Building Models from Laser Scanner Data (2011, result): “Context-based building modeling has been studied by several other researchers (N¨uchter and Hertzberg, 2008; N¨uchter et al., 2003; Pu and Vos-selman, 2006; Cantzler, 2003; Rusu et al., 2008).”
Contextually Guided Semantic Labeling and Search for 3D Point Clouds (2011, background): “Some earlier works Gould et al. (2008); Rusu et al. (2008); Dima et al. (2004); Quigley et al. (2009) have tried to combine shape and color information from multiple sensors for tasks such as object and obstacle detection.”
Understanding and executing instructions for everyday manipulation tasks from the World Wide Web (2010, methodology): “Our environment model [17] is created from 3D laser scans in which objects are detected and classified.”
3D is here: Point Cloud Library (PCL) (2011, methodology): “The filter is based on estimating a set of statistics for the points in a given neighborhood (k = 50 here), and using them to select all points within 1.0·σ distance from the mean distance µ, as inliers (see [6] for more information).”