Collision Avoidance Metric for 3D Camera Evaluation

TL;DR

This paper introduces a new evaluation metric for 3D cameras that better predicts their effectiveness in collision avoidance tasks, addressing limitations of existing metrics.

Contribution

The paper proposes a novel, application-specific metric for evaluating 3D camera performance in collision avoidance, improving real-world relevance.

Findings

The new metric correlates better with collision avoidance success.

Existing metrics like Chamfer distance are less effective for this purpose.

The source code is publicly available for reproducibility.

Abstract

3D cameras have emerged as a critical source of information for applications in robotics and autonomous driving. These cameras provide robots with the ability to capture and utilize point clouds, enabling them to navigate their surroundings and avoid collisions with other objects. However, current standard camera evaluation metrics often fail to consider the specific application context. These metrics typically focus on measures like Chamfer distance (CD) or Earth Mover's Distance (EMD), which may not directly translate to performance in real-world scenarios. To address this limitation, we propose a novel metric for point cloud evaluation, specifically designed to assess the suitability of 3D cameras for the critical task of collision avoidance. This metric incorporates application-specific considerations and provides a more accurate measure of a camera's effectiveness in ensuring safe robot navigation. The source code is available at https://github.com/intrinsic-ai/collision-avoidance-metric.