A Bayesian Modeling Framework for Estimation and Ground Segmentation of Cluttered Staircases

2025-01-07

AI Summary

To address erroneous stair structure identification in cluttered staircases caused by occlusions, sensor noise, and limited field-of-view, this paper proposes a robust stair-state estimation and clutter-free region segmentation method. The approach introduces a novel geometric representation of stairs with infinite width coupled with finite endpoint state modeling; designs a Bayesian fusion framework enabling structural inference under partial observations; and jointly optimizes stair-state estimation and clutter-free segmentation. It integrates multi-sensor data with a model-based ground segmentation algorithm. Evaluated on diverse real-world staircase scenarios using a physical robot platform, the method achieves significantly higher stair localization accuracy than baseline approaches and improves clutter-free region segmentation accuracy by 23.6%, thereby effectively supporting safe climbing decision-making.

Technology Category

• Intelligent Robots: State Estimation
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

Abstract

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.

Problem

• Robot Navigation
• Staircase Detection
• Clutter Discrimination

Innovation

• Bayesian Approach
• Staircase Recognition
• Clutter Differentiation