Learning semantic place labels from occupancy grids using CNNs

By Robert Goeddel, Edwin Olson. 2016

54 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

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. Ala Al-Fuqaha
2. Mohsen Guizani
3. Lei Tai

In Computer Vision and Pattern Recognition

1. Ala Al-Fuqaha
2. Mohsen Guizani
3. Sameh Sorour
4. M. Mohammadi
5. Lei Tai
6. Umair Mohammad
7. Ming Liu

Citations in the corpus

• Deep Learning for IoT Big Data and Streaming Analytics: A Survey (2017, background): “tings for moving materials or performing tasks in hazardous environments. There are many research efforts that have benefited from DL to develop an intelligent control software for mobile robots [228] [229]. Being able to perceive the environment through different kinds of sensors, such as LIDARs and”
• A Survey of Deep Network Solutions for Learning Control in Robotics: From Reinforcement to Imitation (2016, neutral): “el image with a database collected from aerial robots (Lin et al., 2015) and using 2D occupancy grids constructed by Lidar to classify the place categorization (into a corridor, doorway, room, etc.) (Goeddel and Olson, 2016). C. Future directions in DL-aided perception for robotics 1) Multimodal: Th”
• Deep-learning in Mobile Robotics - from Perception to Control Systems: A Survey on Why and Why not (2016, methodology): “…much more possibility for interesting ideas, like matching the ground-level image with a database collected from aerial robots (Lin et al., 2015) and using 2D occupancy grids constructed by Lidar to classify the place categorization (into a corridor, doorway, room, etc.) (Goeddel and Olson, 2016).”