Distraction suppression for vision-based pose estimation at city scales

By Colin McManus, Winston Churchill, Ashley Napier, Ben Davis, Paul M. Newman. 2013

25 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. Ryan W. Wolcott
2. Jason Mars
3. Md E. Haque
4. Lingjia Tang
5. Chang-Hong Hsu
6. Yunqi Zhang
7. Matt Skach
8. Shi-Chieh Lin

In Computer Vision and Pattern Recognition

1. Ryan W. Wolcott
2. Jason Mars
3. Baofu Wu
4. Ryan M. Eustice
5. Sidi Lu
6. Ren Zhong
7. Qingyan Zhang
8. Yongtao Yao

Citations in the corpus

• Visual localization within LIDAR maps for automated urban driving (2014, methodology): “[11] used a similar 3D map with reflectivity information to generate synthetic views for visual distraction suppression.”
• The Architectural Implications of Autonomous Driving: Constraints and Acceleration (2018, methodology): “Therefore, prior map-based localization has beenwidely used to provide localization capability at centimeter-level precision [44, 53, 64, 78, 79, 83], where the surrounding view is transformed into feature descriptions to map the feature points stored in the prior map to identify the location of the”
• Computing Systems for Autonomous Driving: State of the Art and Challenges (2020, methodology): “Several methods for matching maps with the 2-D camera as well as matching 2-D image to the 3-D image are proposed for mapping [115]–[117].”