Visual Localization and Target Perception Based on Panoptic Segmentation

Visual localization is a core part of many computer vision and geospatial perception applications; however, the ever-changing time phase and environment present challenges. Moreover, the ever-enriching spatial data types and sensors create new conditions for visual localization. Based on the prior 3D model and the location sensor, the current study proposes a visual localization method using semantic information. This method integrates panoptic segmentation and the matching network to refine the sensor's position and orientation and complete the target perception. First, the panoptic segmentation and the match network are used together to segment and match the 3D- model-rendered image and the truth image. The matching results are then optimized based on the semantic results. Second, the semantic consistency score is introduced in the RANSAC process to estimate the optimal 6 degree-of-freedom (6DOF) pose. In the final stage, the estimated 6DOF pose, the instance segmentation results, and the depth information are used to locate the target. Experimental results show that the proposed method is a significant improvement on advanced methods for the long-term visual localization benchmark dataset. Additionally, the proposed method is seen to provide improved localization accuracy and is capable of accurately perceiving the target for self-collected data.

Automated summary

This study proposes a visual localization method using semantic information, which integrates panoptic segmentation and the matching network to optimize the position and orientation of the sensor and complete target perception. Experimental results show that the proposed method significantly improves visual localization and accurately perceives the target.