Real Time Incremental Foveal Texture Mapping for Autonomous Vehicles

TL;DR

This paper presents a real-time framework for creating high-resolution textured 3D maps of urban environments, aiding autonomous vehicle navigation and virtual testing, with novel techniques for incremental mapping and texture preservation.

Contribution

The paper introduces a novel real-time incremental mapping method with foveal-processing and ray-filtering to improve map quality and computational efficiency.

Findings

Achieves high-resolution textured 3D maps in real time.

Introduces foveal-processing to reduce computation time.

Provides quantitative metrics for map quality.

Abstract

We propose an end-to-end real time framework to generate high resolution graphics grade textured 3D map of urban environment. The generated detailed map finds its application in the precise localization and navigation of autonomous vehicles. It can also serve as a virtual test bed for various vision and planning algorithms as well as a background map in the computer games. In this paper, we focus on two important issues: (i) incrementally generating a map with coherent 3D surface, in real time and (ii) preserving the quality of color texture. To handle the above issues, firstly, we perform a pose-refinement procedure which leverages camera image information, Delaunay triangulation and existing scan matching techniques to produce high resolution 3D map from the sparse input LIDAR scan. This 3D map is then texturized and accumulated by using a novel technique of ray-filtering which handles occlusion and inconsistencies in pose-refinement. Further, inspired by human fovea, we introduce foveal-processing which significantly reduces the computation time and also assists ray-filtering to maintain consistency in color texture and coherency in 3D surface of the output map. Moreover, we also introduce texture error (TE) and mean texture mapping error (MTME), which provides quantitative measure of texturing and overall quality of the textured maps.