Fast Staircase Detection and Estimation using 3D Point Clouds with Multi-detection Merging for Heterogeneous Robots

TL;DR

This paper introduces a fast, accurate staircase detection method using 3D point clouds, enabling heterogeneous robots to autonomously locate and climb stairs in complex environments, improving robustness and efficiency.

Contribution

The paper presents a novel multi-detection merging algorithm for staircase detection that works across different robot platforms, enhancing detection speed and accuracy.

Findings

Significant increase in detection accuracy

Faster detection times compared to state-of-the-art

Effective operation on heterogeneous robotic systems

Abstract

Robotic systems need advanced mobility capabilities to operate in complex, three-dimensional environments designed for human use, e.g., multi-level buildings. Incorporating some level of autonomy enables robots to operate robustly, reliably, and efficiently in such complex environments, e.g., automatically "returning home" if communication between an operator and robot is lost during deployment. This work presents a novel method that enables mobile robots to robustly operate in multi-level environments by making it possible to autonomously locate and climb a range of different staircases. We present results wherein a wheeled robot works together with a quadrupedal system to quickly detect different staircases and reliably climb them. The performance of this novel staircase detection algorithm that is able to run on the heterogeneous platforms is compared to the current state-of-the-art detection algorithm. We show that our approach significantly increases the accuracy and speed at which detections occur.