Improved localization using visual features and maps for Autonomous Cars

Rangan, Sathya Narayanan Kasturi; Yalla, Veera Ganesh; Bacchet, Davide; Domi, Izzy

doi:10.1109/ivs.2018.8500540

Cited by 4 publications

(2 citation statements)

References 24 publications

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“…More recently, the idea of descriptors has been proposed as an alternative to previously cited distance-based measurement models. In [ 13 ], image-based oriented and rotated brief (ORB) descriptors are used to generate a bag of words, which is then queried for future measurements. A 4-bit descriptor encoding surrounding structural information, such as intersections and buildings, is introduced by Yan et al [ 14 ].…”

Section: Related Workmentioning

confidence: 99%

Road Descriptors for Fast Global Localization on Rural Roads Using OpenStreetMap

Ninan,

Rathinam

2023

Sensors

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Accurate pose estimation is a fundamental ability that all mobile robots must posses in order to navigate a given environment. Much like a human, this ability is dependent on the robot’s understanding of a given scene. For autonomous vehicles (AVs), detailed 3D maps created beforehand are widely used to augment the perceptive abilities and estimate pose based on current sensor measurements. This approach, however, is less suited for rural communities that are sparsely connected and cover large areas. Topological maps such as OpenStreetMap have proven to be a useful alternative in these situations. However, vehicle localization using these maps is non-trivial, particularly for the global localization task, where the map spans large areas. To deal with this challenge, we propose road descriptors along with an initialization technique for localization that allows for fast global pose estimation. We test our algorithms on (real world) maps and benchmark them against other map-based localization as well as SLAM algorithms. Our results show that the proposed method can narrow down the pose to within 50 cm of the ground truth significantly faster than the state-of-the-art methods.

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Section: Related Workmentioning

confidence: 99%

Road Descriptors for Fast Global Localization on Rural Roads Using OpenStreetMap

Ninan,

Rathinam

2023

Sensors

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“…It is therefore the sensor with the most potential for localization. Rangan [9] builds up a local map with camera features and associates them with GNSS coordinates. During localization, their system matches the features of the current camera frame against a GNSS-queried subset of the map features.…”

Section: Related Workmentioning

confidence: 99%