Volumetric Occupancy Detection: A Comparative Analysis of Mapping Algorithms

TL;DR

This paper provides a comprehensive comparison of volumetric occupancy detection algorithms like OctoMap, SkiMap, and Voxblox, using simulation and automated metrics to evaluate their performance for collaborative robotics security.

Contribution

It introduces a detailed evaluation framework for volumetric mapping algorithms, including parameter analysis, filling a gap in existing assessment methods.

Findings

OctoMap outperforms others in accuracy

Parameter tuning significantly affects performance

Automated metrics enable consistent evaluation

Abstract

Despite the growing interest in innovative functionalities for collaborative robotics, volumetric detection remains indispensable for ensuring basic security. However, there is a lack of widely used volumetric detection frameworks specifically tailored to this domain, and existing evaluation metrics primarily focus on time and memory efficiency. To bridge this gap, the authors present a detailed comparison using a simulation environment, ground truth extraction, and automated evaluation metrics calculation. This enables the evaluation of state-of-the-art volumetric mapping algorithms, including OctoMap, SkiMap, and Voxblox, providing valuable insights and comparisons through the impact of qualitative and quantitative analyses. The study not only compares different frameworks but also explores various parameters within each framework, offering additional insights into their performance.