Visual localization within LIDAR maps for automated urban driving

By Ryan W. Wolcott, Ryan M. Eustice. 2014

399 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Environmental Engineering, Geology

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Citations in the corpus

A Survey of Autonomous Driving: Common Practices and Emerging Technologies (2019, methodology): “A monocular camera was used to localize the vehicle in a point cloud map in [121].”
Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age (2016, neutral): “[260] is an initial step in this direction.”
Street-view change detection with deconvolutional networks (2016, methodology): “Subsequently, vehicles equipped with lowresolution LiDAR scanners may use the maps to achieve the precise vehicle localization required for autonomous navigation (Wolcott and Eustice 2014).”
Mobile Edge Intelligence and Computing for the Internet of Vehicles (2019, methodology): “, via the powerful CNN [44], and can also be used for visionbased localization [51]; LiDAR is applied for 3D mapping and localization [52]; Radar and Sonar are used for obstacle detection.”
Cross-View Policy Learning for Street Navigation (2019, background): “The research on artificial agent navigation can be applied to real world domains ranging from the neuroscience of grid and place cells in mammals [3, 9] to the autonomy of indoor and outdoor mobile robots [49, 39, 31, 38, 45, 32].”
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”
Fast LIDAR localization using multiresolution Gaussian mixture maps (2015, methodology): “In order to capture the variance of our LIDAR scanner and reduce discretization errors, we blur these z-points with neighboring “columns” with a Gaussian kernel.”
Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving (2017, neutral): “in that sense is multi-modality where a map could be built by an expensive 3D laser but then exploited by a simple camera as proposed in [273].”
A Survey of the State-of-the-Art Localization Techniques and Their Potentials for Autonomous Vehicle Applications (2018, methodology): “On the other hand, addressing the high implementation cost of LiDAR techniques, Wolcott and Eustice [40] investigated the use of camera-based localization within pre-existing LiDAR maps.”
A Theoretical Foundation of Network Localization and Navigation (2018, background): “Localizing mobile nodes with unknown positions (called agents) can be accomplished by utilizing two types of measurements, namely, inter-node and intra-node measurements, together with prior knowledge [54]–[58] Internode measurements refer to those between nodes in a network through radio-frequency ”