Clutter Slices Approach for Identification-on-the-fly of Indoor Spaces

TL;DR

This paper introduces a novel, sensor-independent method for autonomous indoor space classification using clutter signatures, enabling robots to efficiently survey dynamic construction environments with high accuracy.

Contribution

The paper presents a new identification-on-the-fly approach leveraging clutter signatures for coarse indoor space classification, enhancing autonomous perception in construction environments.

Findings

Achieved 93.6% accuracy on clutter slices dataset.

Proposed method is sensor-independent and generalizable.

Enables autonomous agents to better perceive dynamic indoor spaces.

Abstract

Construction spaces are constantly evolving, dynamic environments in need of continuous surveying, inspection, and assessment. Traditional manual inspection of such spaces proves to be an arduous and time-consuming activity. Automation using robotic agents can be an effective solution. Robots, with perception capabilities can autonomously classify and survey indoor construction spaces. In this paper, we present a novel identification-on-the-fly approach for coarse classification of indoor spaces using the unique signature of clutter. Using the context granted by clutter, we recognize common indoor spaces such as corridors, staircases, shared spaces, and restrooms. The proposed clutter slices pipeline achieves a maximum accuracy of 93.6% on the presented clutter slices dataset. This sensor independent approach can be generalized to various domains to equip intelligent autonomous agents in better perceiving their environment.