Crossview Mapping with Graph-based Geolocalization on City-Scale Street Maps

Ye, Zhichao; Bao, Chong Gao; Liu, Xinyang; Bao, Hujun; Cui, Zhaopeng; Zhang, Guofeng

doi:10.1109/icra46639.2022.9811743

Cited by 4 publications

(1 citation statement)

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“…Other semantic-aided methods use OpenStreetMap, which is readily available and requires little memory. These methods compare observed roads [4]- [6], buildings [7], [8], or both [9] to determine where the vehicle is inside the reference map. Overall, most semantic-aided methods are restricted to working only in urban or suburban settings.…”

Section: Semantic-aided Methodsmentioning

confidence: 99%

Global Localization in Unstructured Environments using Semantic Object Maps Built from Various Viewpoints

Ankenbauer¹,

C.²,

How³

2023

Preprint

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We present a novel framework for global localization and guided relocalization of a vehicle in an unstructured environment. Compared to existing methods, our pipeline does not rely on cues from urban fixtures (e.g., lane markings, buildings), nor does it make assumptions that require the vehicle to be navigating on a road network. Instead, we achieve localization in both urban and non-urban environments by robustly associating and registering the vehicle's local semantic object map with a compact semantic reference map, potentially built from other viewpoints, time periods, and/or modalities. Robustness to noise, outliers, and missing objects is achieved through our graph-based data association algorithm. Further, the guided relocalization capability of our pipeline mitigates drift inherent in odometry-based localization after the initial global localization. We evaluate our pipeline on two publiclyavailable, real-world datasets to demonstrate its effectiveness at global localization in both non-urban and urban environments. The Katwijk Beach Planetary Rover dataset [1] is used to show our pipeline's ability to perform accurate global localization in unstructured environments. Demonstrations on the KITTI dataset [2] achieve an average pose error of 3.8 m across all 35 localization events on Sequence 00 when localizing in a reference map created from aerial images. Compared to existing works, our pipeline is more general because it can perform global localization in unstructured environments using maps built from different viewpoints.

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