Categorized Grid and Unknown Space Causes for LiDAR-based Dynamic Occupancy Grids

TL;DR

This paper introduces a Categorized Grid for LiDAR-based dynamic occupancy maps that labels space with semantic and cause-based information, enhancing environment understanding in real-world scenarios.

Contribution

It extends existing occupancy grids by adding semantic labels and cause-based categorization of unknown space, improving perception and situation awareness.

Findings

Demonstrated improved environment understanding in real-world tests.

Effective categorization of unknown space causes.

Enhanced semantic labeling of occupied regions.

Abstract

Occupancy Grids have been widely used for perception of the environment as they allow to model the obstacles in the scene, as well as free and unknown space. Recently, there has been a growing interest in the unknown space due to the necessity of better understanding the situation. Although Occupancy Grids have received numerous extensions over the years to address emerging needs, currently, few works go beyond the delimitation of the unknown space area and seek to incorporate additional information. This work builds upon the already well-established LiDAR-based Dynamic Occupancy Grid to introduce a complementary Categorized Grid that conveys its estimation using semantic labels while adding new insights into the possible causes of unknown space. The proposed categorization first divides the space by occupancy and then further categorizes the occupied and unknown space. Occupied space is labeled based on its dynamic state and reliability, while the unknown space is labeled according to its possible causes, whether they stem from the perception system's inherent constraints, limitations induced by the environment, or other causes. The proposed Categorized Grid is showcased in real-world scenarios demonstrating its usefulness for better situation understanding.