A Triangulation-Based Visual Localization for Field Robots

Dear Editor, Visual localization relies on local features and searches a pre-stored GPS-tagged image database to retrieve the reference image with the highest similarity in feature spaces to predict the current location 1–3. For the conventional methods 4–6, local features are generally explored by multiple-stage feature extraction which first detects and then describes key-point features 4, 7. The multiple-stage feature extraction is redundantly implemented, which is not memory and run-time efficient. Its performance degrades with challenging conditions such as poor lighting and weather variations (as shown in Fig. 1(a)) because the multiple-stage design may lose information in the quantization step which produces inadequately key-point features for matching. Another critical issue for existing visual localization is that most of the conventional methods are one-directional-based approaches, which only use one-directional images (front images) to search and match GPS-tag references 4, 8. With the increase of database size, one-directional inputs can be homogeneous which makes it difficult for the localization algorithms to work robustly (as shown in Fig. 1(b)).