Long-term Visual Localization using Semantically Segmented Images
This addresses robust cross-seasonal localization for autonomous vehicles, though it is incremental as it builds on existing semantic segmentation techniques.
The paper tackles long-term visual localization for autonomous vehicles by using semantically segmented images and 3-D point maps, eliminating the need for traditional feature descriptors like SIFT. It achieves localization errors below 1 meter, performing on par with SIFT-based methods while reducing storage space.
Robust cross-seasonal localization is one of the major challenges in long-term visual navigation of autonomous vehicles. In this paper, we exploit recent advances in semantic segmentation of images, i.e., where each pixel is assigned a label related to the type of object it represents, to attack the problem of long-term visual localization. We show that semantically labeled 3-D point maps of the environment, together with semantically segmented images, can be efficiently used for vehicle localization without the need for detailed feature descriptors (SIFT, SURF, etc.). Thus, instead of depending on hand-crafted feature descriptors, we rely on the training of an image segmenter. The resulting map takes up much less storage space compared to a traditional descriptor based map. A particle filter based semantic localization solution is compared to one based on SIFT-features, and even with large seasonal variations over the year we perform on par with the larger and more descriptive SIFT-features, and are able to localize with an error below 1 m most of the time.