| Full text | |
| Author(s): |
Total Authors: 2
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| Affiliation: | [1] Univ Sao Paulo, Mobile Robot Lab, BR-13560970 Sao Carlos, SP - Brazil
Total Affiliations: 1
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| Document type: | Journal article |
| Source: | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS; v. 17, n. 2, p. 420-429, FEB 2016. |
| Web of Science Citations: | 30 |
| Abstract | |
Localization is an important component of autonomous vehicles, as it enables the accomplishment of tasks, such as path planning and navigation. Although vehicle position can be obtained by GNSS devices, they are susceptible to errors and satellite signal unavailability in urban scenarios. Several map- aided localization solution methods have been proposed in the literature, but mostly for indoor environments. Maps used for localization store relevant environmental features that are extracted by a detection method. However, many feature detection methods do not consider the presence of dynamic obstacles or occlusions in the environment, which can impair the localization performance. In order to detect curbs even in occluding scenes, we developed a method based on ring compression analysis and least trimmed squares. For road marking detection, we developed a modified version of the Otsu thresholding method to segment road painting from road surfaces. Finally, the feature detection methods were integrated with a Monte Carlo localization method to estimate the vehicle position. Experimental tests in urban streets have been used to validate the proposed approach with favorable results. (AU) | |
| FAPESP's process: | 12/02354-1 - Localization for autonomous vehicles in urban environment using continuous occupancy maps |
| Grantee: | Alberto Yukinobu Hata |
| Support Opportunities: | Scholarships in Brazil - Doctorate |
| FAPESP's process: | 14/09096-3 - Localização de Veículos utilizando Mapas continuos de processos Gaussianos |
| Grantee: | Alberto Yukinobu Hata |
| Support Opportunities: | Scholarships abroad - Research Internship - Doctorate |