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