Close-range scene segmentation and reconstruction of 3D point cloud maps for mobile manipulation in domestic environments

By Radu Bogdan Rusu, Nico Blodow, Zoltán-Csaba Márton, Michael Beetz. 2009

234 citations recorded by the source corpus.

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

How researchers see this paper

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

In Aerospace Engineering

1. Ashutosh Saxena
2. Hema Swetha Koppula
3. Bhaskara Marthi
4. Ronald E. Parr
5. Julian Mason
6. Min Sun
7. Robert W. Platt
8. Andreas ten Pas

In Computer Vision and Pattern Recognition

1. Luc Van Gool
2. Xiaogang Wang
3. Li Fei-Fei
4. Jitendra Malik
5. Trevor Darrell
6. Dacheng Tao
7. Ming-Hsuan Yang
8. Andrew Zisserman

In Control and Systems Engineering

1. Silvio Savarese
2. Ashutosh Saxena
3. Robert W. Platt
4. Andreas ten Pas
5. Marcus Gualtieri
6. Hema Swetha Koppula
7. Sergey Levine
8. Rudhir Gupta

Citations in the corpus

• Learning human activities and object affordances from RGB-D videos (2012, methodology): “For microwaving food, they were: 1) Open microwave door, 2) Place food inside, 3) Close microwave door. scrubber, stationary}.”
• Pick and Place Without Geometric Object Models (2017, background): “Others have extended these ideas to segmentation and manipulation problems [7], [8], [9].”
• Indoor mapping using planes extracted from noisy RGB-D sensors (2012, background): “There has been much interest in building 3D maps of indoor environments, which are needed for a wide range of robotic applications such as navigation [1], manipulation [2], augmented reality [3], etc.”
• Revisiting Point Cloud Shape Classification with a Simple and Effective Baseline (2021, background): “Processing 3D point cloud data accurately is crucial in many applications including autonomous driving (Navarro-Serment et al., 2010; Kidono et al., 2011), robotics (Rusu et al., 2009; Correll et al., 2016; Mousavian et al., 2019) and scene understanding (Aldoma et al., 2012).”
• Unsupervised discovery of object classes with a mobile robot (2014, background): “[8] focus on segmentation of objects at close range in tabletop settings.”
• Object disappearance for object discovery (2012, background): “See, e.g., Rusu et al. [23] and Trevor et al. [24].”
• Real-Time Plane Segmentation Using RGB-D Cameras (2012, methodology): “We follow a similar approach as in [9] and [4] for detecting objects.”
• Depth-Encoded Hough Voting for Joint Object Detection and Shape Recovery (2010, background): “Other approaches assume that additional information about the object is available in both training and testing (videos, 3D range data) [20,5].”