Abstract:
In this paper, we address the problem of optimally fusing multiple heterogeneous and asynchronous sensors for use in 3D mapping and localization of autonomous vehicles. T...Show MoreMetadata
Abstract:
In this paper, we address the problem of optimally fusing multiple heterogeneous and asynchronous sensors for use in 3D mapping and localization of autonomous vehicles. To this end, based on the factor graph-based optimization framework, we design a modular sensor-fusion system that allows for efficient and accurate incorporation of multiple navigation sensors operating at different sampling rates. In particular, we develop a general method of out-of-sequence (asynchronous) measurement alignment to incorporate heterogeneous sensors into a factor graph for mapping and localization in 3D, without requiring the addition of new graph nodes, thus allowing the graph to have an overall reduced complexity. The proposed sensor-fusion system is validated on a real-world experimental dataset, in which the asynchronous-measurement alignment is shown to have an improved performance when compared to a naive approach without alignment.
Date of Conference: 21-25 May 2018
Date Added to IEEE Xplore: 13 September 2018
ISBN Information:
Electronic ISSN: 2577-087X