Visual information is a valuable asset in any perception scheme designed for an intelligent transportation system. In this regard, the camera-based recognition of locations provides a higher situational awareness of the environment, which is very useful for varied localization solutions typically needed in long-term autonomous navigation, such as loop closure detection and visual odometry or SLAM correction. In this paper we present OpenABLE, an open-source toolbox contributed to the community with the aim of helping researchers in the application of these kinds of life-long localization algorithms. The implementation follows the philosophy of the topological place recognition method named ABLE, including several new features and improvements. These functionalities allow to match locations using different global image description methods and several configuration options, which enable the users to control varied parameters in order to improve the performance of place recognition depending on their specific problem requisites. The applicability of our toolbox in visual localization purposes for intelligent vehicles is validated in the presented results, jointly with comparisons to the main state-of-the-art methods.
['Roberto Arroyo', 'Pablo F. Alcantarilla', 'Luis Miguel Bergasa', 'Eduardo Romera']
OpenABLE: An open-source toolbox for application in life-long visual localization of autonomous vehicles