Made to measure: Bespoke landmarks for 24-hour, all-weather localisation with a camera
By Chris Linegar, Winston Churchill, Paul M. Newman. 2016
88 citations recorded by the source corpus.
Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Geology
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
- Marc Pollefeys
- Torsten Sattler
- Josef Sivic
- Abhinav Kumar Gupta
- Tomás Pajdla
- Akihiko Torii
- Ali Farhadi
- Alex Kendall
In Computer Vision and Pattern Recognition
- Luc Van Gool
- Jitendra Malik
- Abhinav Kumar Gupta
- Dacheng Tao
- Trevor Darrell
- Andrew Zisserman
- Xiaogang Wang
- Li Fei-Fei
In Geology
- Abhinav Kumar Gupta
- Marc Pollefeys
- Ali Farhadi
- Dieter Fox
- Daniel Gordon
- Tomás Pajdla
- Josef Sivic
- William P. Maddern
Citations in the corpus
- Learning to Drive in a Day (2018, methodology): “Localisation such as [15] facilitates control of the vehicle within the mapped environment, while perception methods such as semantic segmentation [1] enable the robot to interpret the scene.”
- Target-driven visual navigation in indoor scenes using deep reinforcement learning (2016, background): “Besides, our approach does not require supervised training for recognizing distinctive landmarks, unlike e.g., [21], [22].”
- IQA: Visual Question Answering in Interactive Environments (2017, methodology): “Other navigation methods include map building guided by humans [34, 69] and map-less approaches [19, 24, 39, 43, 47, 52, 66, 71, 95] which use techniques such as obstacle avoidance and feature matching and depend upon implicit representations of the world to perform navigation.”
- Find your own way: Weakly-supervised segmentation of path proposals for urban autonomy (2016, methodology): “We plan to integrate the network with a planning framework that includes our previous work on topometric localisation across experiences [35] as well as our semantic map-guided approach for traffic light detection [36] to enable fully autonomous driving in complex urban environments.”
- Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving (2017, unknown): “In [164], one SVM per feature is trained across several images.”
- Benchmarking 6DOF Outdoor Visual Localization in Changing Conditions (2018, methodology): “They learn features with stable appearance over time [17, 47, 49], train classifiers for place recognition [12, 28, 37, 76], and train CNNs to regress 2D-3D matches [8, 9, 60] or camera poses [19, 31, 74].”