Dense Semantic 3D Map Based Long-Term Visual Localization with Hybrid Features
This addresses the problem of reliable localization for applications like robotics or autonomous vehicles in changing environments, representing an incremental improvement.
The paper tackles robust long-term visual localization under appearance variations by proposing a method that uses hybrid handcrafted and learned features with a dense semantic 3D map, achieving state-of-the-art results on benchmarks.
Visual localization plays an important role in many applications. However, due to the large appearance variations such as season and illumination changes, as well as weather and day-night variations, it's still a big challenge for robust long-term visual localization algorithms. In this paper, we present a novel visual localization method using hybrid handcrafted and learned features with dense semantic 3D map. Hybrid features help us to make full use of their strengths in different imaging conditions, and the dense semantic map provide us reliable and complete geometric and semantic information for constructing sufficient 2D-3D matching pairs with semantic consistency scores. In our pipeline, we retrieve and score each candidate database image through the semantic consistency between the dense model and the query image. Then the semantic consistency score is used as a soft constraint in the weighted RANSAC-based PnP pose solver. Experimental results on long-term visual localization benchmarks demonstrate the effectiveness of our method compared with state-of-the-arts.