Practice makes perfect? Managing and leveraging visual experiences for lifelong navigation

By Winston Churchill, Paul M. Newman. 2012

134 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

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In Aerospace Engineering

In Computer Vision and Pattern Recognition

Citations in the corpus

Lifelong GAN: Continual Learning for Conditional Image Generation (2019, background): “Recent efforts [30, 4, 9] have demonstrated how discriminative models could be incrementally learnt for a sequence of tasks.”
The future of employment: How susceptible are jobs to computerisation? (2017, neutral): “be compared (Churchill and Newman, 2012).”
Visual Place Recognition: A Survey (2016, background): “This image (from [228]) shows the number of robot " experiences " stored during repeated traversals of a path over a number of months.”
On the performance of ConvNet features for place recognition (2015, background): “Methods that address the place recognition problem span from matching sequences of images [1], [4]–[7], transforming images to become invariant against common scene changes such as shadows [2], [8], [10]–[12], learning how environments change over time and predicting these changes in image space [3]”
Visual SLAM for Automated Driving: Exploring the Applications of Deep Learning (2018, background): “More recently [13] and [45] proposed two concurrent and promising approaches.”
Illumination Invariant Imaging : Applications in Robust Vision-based Localisation , Mapping and Classification for Autonomous Vehicles (2014, methodology): “This approach builds upon the experience-based navigation framework presented in [31], which represents the map as a series of stereo visual odometry sequences, dubbed experiences, along with metric localisations between different experiences of the same locaLAPS (Illumination Invariant) NovAtel INS”
Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving (2017, unknown): “We refer the readers to the description of the following works in Section IV which could be utilized by several vehicles in a cloud computation: [148], [150], [154], [161], [163], [166]–[168].”
Light-weight localization for vehicles using road markings (2013, background): “This may be impractical for large scale use but may be a good approach for reliable localization on a few specific routes, such as for a specific commute [4].”