Depth camera based indoor mobile robot localization and navigation

By Joydeep Biswas, Manuela M. Veloso. IEEE International Conference on Robotics and Automation, 2012

365 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/e8df9569117dd72b2ddd9236b111b9b773df06e8

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. Carlos Bermejo
2. Tristan Braud
3. Shih-Yang Su
4. Abhishek Kumar
5. Zijun Lin
6. Lin Wang
7. Dianlei Xu
8. Lik-Hang Lee

In Computer Vision and Pattern Recognition

1. Carlos Bermejo
2. Chun-Yi Lee
3. Carlo Fischione
4. Tristan Braud
5. Shih-Yang Su
6. Abhishek Kumar
7. Zijun Lin
8. Lin Wang

In Electrical and Electronic Engineering

1. Carlo Fischione
2. Adriano J. C. Moreira
3. Lauri Wirola
4. C. Laoudias
5. Sunwoo Kim
6. Pan Hui
7. Sangwoo Lee

Citations in the corpus

• Virtual-to-Real: Learning to Control in Visual Semantic Segmentation (2018, background): “Interpreting and representing visual inputs to perform actions and interact with objects, however, are challenging for robots in unstructured environments as colored images are typically complex and noisy [ Maier et al. , 2012; Biswas and Veloso, 2012].”
• A Survey of Enabling Technologies for Network Localization, Tracking, and Navigation (2018, methodology): “Between these two extremes, camera-based solutions, especially those developed around Microsoft Kinect including [253]–[256], provide a good balance between cost (in terms of equipment price and survey time) and map accuracy.”
• All One Needs to Know about Metaverse: A Complete Survey on Technological Singularity, Virtual Ecosystem, and Research Agenda (2021, background): “pose and obtain a geometrically consistent map, also called close-loop detection [312].”