Coarse-To-Fine Visual Localization Using Semantic Compact Map
This addresses the problem of efficient and memory-light localization for autonomous vehicles, though it appears incremental as it builds on existing semantic and particle filter methods.
The paper tackles robust visual localization for urban vehicles by proposing a coarse-to-fine system using a semantic compact map, achieving comparable accuracy with a 10 KB map for a 3.7 km trajectory compared to baselines.
Robust visual localization for urban vehicles remains challenging and unsolved. The limitation of computation efficiency and memory size has made it harder for large-scale applications. Since semantic information serves as a stable and compact representation of the environment, we propose a coarse-to-fine localization system based on a semantic compact map. Pole-like objects are stored in the compact map, then are extracted from semantically segmented images as observations. Localization is performed by a particle filter, followed by a pose alignment module decoupling translation and rotation to achieve better accuracy. We evaluate our system both on synthetic and realistic datasets and compare it with two baselines, a state-of-art semantic feature-based system, and a traditional SIFT feature-based system. Experiments demonstrate that even with a significantly small map, such as a 10 KB map for a 3.7 km long trajectory, our system provides a comparable accuracy with the baselines.