散らかった階段の推定と領域分割のためのベイズモデリングフレームワーク
A Bayesian Modeling Framework for Estimation and Ground Segmentation of Cluttered Staircases
階段の構造を無限幅の表現と有限の端点状態でモデル化し、ベイズ推論でノイズの多い観測を統合することで、遮蔽や部分観測があっても階段の位置を高精度に推定し、さらに障害物のない領域を分割する手法を提案した。
著者: Prasanna Sriganesh, Burhanuddin Shirose, Matthew Travers
分類: cs.RO
原文アブストラクト
Autonomous robot navigation in complex environments requires robust perception as well as high-level scene understanding due to perceptual challenges, such as occlusions, and uncertainty introduced by robot movement. For example, a robot climbing a cluttered staircase can misinterpret clutter as a step, misrepresenting the state and compromising safety. This requires robust state estimation methods capable of inferring the underlying structure of the environment even from incomplete sensor data. In this paper, we introduce a novel method for robust state estimation of staircases. To address the challenge of perceiving occluded staircases extending beyond the robot's field-of-view, our approach combines an infinite-width staircase representation with a finite endpoint state to capture the overall staircase structure. This representation is integrated into a Bayesian inference framework to fuse noisy measurements enabling accurate estimation of staircase location even with partial observations and occlusions. Additionally, we present a segmentation algorithm that works in conjunction with the staircase estimation pipeline to accurately identify clutter-free regions on a staircase. Our method is extensively evaluated on real robot across diverse staircases, demonstrating significant improvements in estimation accuracy and segmentation performance compared to baseline approaches.
