LiDAR-based localization using universal encoding and memory-aware regression

Visual localization is critical to many robotics and computer vision applications. Absolute pose regression performs localization by encoding scene features followed by pose regression, which has achieved impressive results in localization. It recovers 6-DoF poses from captured scene data alone. However, current methods suffer from being retrained with specific source data whenever the scene changes, resulting in expensive computational costs, data privacy disclosure, and unreliable localization caused by the inability to memorize all data. In this paper, we propose a novel LiDAR-based absolute pose regression network with universal encoding to avoid redundant retraining and the loss of data privacy. Specifically, we propose using universal feature encoding for different scenes. Only the regressor needs to be retrained to achieve higher efficiency, and the training is performed using the encoded features without source data, which preserves data privacy. Then, we propose a memory regressor for memory-aware regression, where the hidden unit numbers in the regressor determine the memorization capacity. It can be used to derive and improve the upper bound of the capacity to enable more reliable localization. Then, it is possible to modify the regressor structure to adapt different memorization capacity requirements for different scene sizes. Extensive experiments on outdoor and indoor datasets validated the above analyses and demonstrated the effectiveness of the proposed method