Semantic 3D Object Maps for Everyday Manipulation in Human Living Environments

By Radu Bogdan Rusu. 2010

1,055 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Control and Systems Engineering

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Citations in the corpus

A Survey of Autonomous Driving: Common Practices and Emerging Technologies (2019, neutral): “Traditional methods often used euclidean clustering [158] or region-growing methods [159] for grouping points into objects.”
Autoware on Board: Enabling Autonomous Vehicles with Embedded Systems (2018, methodology): “The point cloud is pre-processed and segmented using the nearest neighbors algorithm [18].”
Predicting the Influence of Rain on LIDAR in ADAS (2019, methodology): “The segmentation algorithm was the Euclidean cluster extraction algorithm [18] provided with the Point Cloud Library [19].”
An Overview to Visual Odometry and Visual SLAM: Applications to Mobile Robotics (2015, methodology): “Some of the most well-known 3D Descrip-tor methods are Point Feature Histogram (PFH) [98], Fast Point Feature Histogram (FPFH) [99], 3D Shape Context (3DSC) [46], Unique Shape Context (USC) [112] and Sig-natures of Histograms of Orientations (SHOT) [112].”
Perception, Planning, Control, and Coordination for Autonomous Vehicles (2017, background): “, Euclidean distance [34,35] or surface normals [36].”
Fast segmentation of 3D point clouds: A paradigm on LiDAR data for autonomous vehicle applications (2017, background): “Examples include the euclidean cluster extraction [9] whose implementation can be found in the point cloud library (PCL) [10], DBSCAN [11], and MeanShift [12].”
Field-based architectural traits characterisation of maize plant using time-of-flight 3D imaging (2019, methodology): “The point cloud was evenly sliced into thin layers along the plant growth direction (the X axis) using the PassThrough filter in PCL (Rusu, 2010), and Euclidean clusters (Rusu, 2010) were extracted in each layer (Fig.”
Nonmyopic View Planning for Active Object Classification and Pose Estimation (2014, methodology): “Neighboring points within a fixed radius of every keypoint are used to compute Fast Point Feature Histograms [38].”
A Hand Motion-guided Articulation and Segmentation Estimation (2020, methodology): “We apply Euclidian clustering [21] on the extracted point cloud and select the clusters nearby the hand position in the first frame with a threshold distance.”
A Hybrid Vision-Map Method for Urban Road Detection (2017, methodology): “This feature describes a local surface variation and was applied to 3D semantic perception in [41].”
Fast LIDAR localization using multiresolution Gaussian mixture maps (2015, background): “For example, Rusu [12] and Aghamohammadi et al. [13] presented different features that can be extracted and matched from raw point cloud points.”
Natural Language Acquisition and Grounding for Embodied Robotic Systems (2017, background): “• Fshape : object → R(32); Fshape(o) gives a 32 bin rotation and translation invariant shape fast point feature histogram (FPFH) per object (Rusu 2009).”
Non Destructive Method for Biomass Prediction Combining TLS Derived Tree Volume and Wood Density (2015, methodology): “We implemented an Euclidean clustering operation [47] and removed the clusters containing only one point.”