Persistent merging of maps created by different sensor modalities is an insufficiently addressed problem. Current approaches either rely on appearance-based features which may suffer from lighting and viewpoint changes or require pre-registration between all sensor modalities used. This work presents a framework using structural descriptors for matching LIDAR point-cloud maps and sparse vision keypoint maps. The matching algorithm works independently of the sensors' viewpoint and varying lighting and does not require pre-registration between the sensors used. Furthermore, we employ the approach in a novel vision-laser map-merging algorithm. We analyse a range of structural descriptors and present results of the method integrated within a full mapping framework. Despite the fact that we match between the visual and laser domains, we can successfully perform map-merging using structural descriptors. The effectiveness of the presented structure-based vision-laser matching is evaluated on the public KITTI dataset and furthermore demonstrated on a map merging problem in an industrial site.
['Abel Gawel', 'Titus Cieslewski', 'Renaud Dubé', 'Mike Bosse', 'Roland Siegwart', 'Juan I. Nieto']
Structure-based vision-laser matching