A multirange architecture for collision‐free off‐road robot navigation
By Pierre Sermanet, Raia Hadsell, Marco Scoffier, Matthew Koichi Grimes, Jan Ben, Ayse Erkan, C. Crudele, Urs Miller, Yann LeCun. 2009
48 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
- Yann LeCun
- Vikash Kumar
- Pierre Sermanet
- Koray Kavukcuoglu
- Sergey Levine
- Raia Hadsell
- Markus Wulfmeier
- Jonathan Tompson
In Computer Vision and Pattern Recognition
- Luc Van Gool
- Sergey Levine
- Xiaogang Wang
- Andrew Zisserman
- Trevor Darrell
- Jitendra Malik
- Dacheng Tao
- Andrea Vedaldi
Citations in the corpus
- A Survey of Deep Network Solutions for Learning Control in Robotics: From Reinforcement to Imitation (2016, neutral): “ to accomplish the grasping task. The output of the deep-learning model is the grasp success probability. Besides visual servoing, several works used CNN models to realize outdoor scene segmentation (Sermanet et al., 2009; Hadsell et al., 2009) based on stereo vision. Multi-range traversability and ”
- Learning Latent Plans from Play (2019, background): “[54], run a simple low-level planner at high-frequency while running a more sophisticated higher-level planner at low-frequency.”
- Watch this: Scalable cost-function learning for path planning in urban environments (2016, background): “Furthermore, additional constraints have to be introduced to dissolve the inherent ambiguity of reward structures for given demonstrator behaviour [5].”
- Learning long‐range vision for autonomous off‐road driving (2009, methodology): “Details of the full system, including architecture, short-range perception and planning, and long-range planning, are given in Sermanet et al. (2009). We present experimental results obtained by running the robot on two courses with the long-range vision turned on and turned off.”
- FPGA-Based Accelerators of Deep Learning Networks for Learning and Classification: A Review (2019, background): “image classification [27]–[32], face detection and recognition [33], [34], robot navigation [35]–[37], real-time multiple object tracking [38], financial forecasting [39], and medical diagnosis systems [40]–[42], to name a few.”