Augmented Environment Representations with Complete Object Models
Krishnananda Prabhu Sivananda, Francesco Verdoja, Ville Kyrki

TL;DR
This paper introduces a multi-layer environment representation for indoor robotics that combines 3D geometry, semantics, and object models, enhancing understanding and navigation safety.
Contribution
It presents a novel pipeline that integrates 3D metric-semantic layers, occupancy, and object models with a shape matching approach, outperforming existing methods.
Findings
Shape matching outperforms deep learning in object completion.
Pipeline performs well from simulation to real-world scenarios.
Multi-layer maps improve robotic navigation safety.
Abstract
While 2D occupancy maps commonly used in mobile robotics enable safe navigation in indoor environments, in order for robots to understand and interact with their environment and its inhabitants representing 3D geometry and semantic environment information is required. Semantic information is crucial in effective interpretation of the meanings humans attribute to different parts of a space, while 3D geometry is important for safety and high-level understanding. We propose a pipeline that can generate a multi-layer representation of indoor environments for robotic applications. The proposed representation includes 3D metric-semantic layers, a 2D occupancy layer, and an object instance layer where known objects are replaced with an approximate model obtained through a novel model-matching approach. The metric-semantic layer and the object instance layer are combined to form an augmented…
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Taxonomy
TopicsRobotics and Sensor-Based Localization · Video Surveillance and Tracking Methods · Advanced Image and Video Retrieval Techniques
