iSimLoc: Visual Global Localization for Previously Unseen Environments with Simulated Images

TL;DR

iSimLoc is a hierarchical visual localization method that robustly matches images under varying conditions and viewpoints, enabling fast and accurate global re-localization in unseen environments, suitable for drone navigation.

Contribution

The paper introduces iSimLoc, a novel hierarchical approach that improves visual re-localization accuracy and speed across diverse and challenging environments using simulated images.

Findings

Achieves 88.7% and 83.8% successful retrieval rates on two datasets.

Operates with an average inference time of 1.5 seconds.

Outperforms previous methods significantly in robustness and efficiency.

Abstract

The visual camera is an attractive device in beyond visual line of sight (B-VLOS) drone operation, since they are low in size, weight, power, and cost, and can provide redundant modality to GPS failures. However, state-of-the-art visual localization algorithms are unable to match visual data that have a significantly different appearance due to illuminations or viewpoints. This paper presents iSimLoc, a condition/viewpoint consistent hierarchical global re-localization approach. The place features of iSimLoc can be utilized to search target images under changing appearances and viewpoints. Additionally, our hierarchical global re-localization module refines in a coarse-to-fine manner, allowing iSimLoc to perform a fast and accurate estimation. We evaluate our method on one dataset with appearance variations and one dataset that focuses on demonstrating large-scale matching over a long flight in complicated environments. On our two datasets, iSimLoc achieves 88.7\% and 83.8\% successful retrieval rates with 1.5s inferencing time, compared to 45.8% and 39.7% using the next best method. These results demonstrate robust localization in a range of environments.