Proceedings ArticleDOI
Autonomous learning of vision-based layered object models on mobile robots
Xiang Li,Mohan Sridharan,Shiqi Zhang +2 more
- pp 6239-6244
TLDR
This paper presents an approach that enables a mobile robot to autonomously learn layered models for environmental objects using temporal, local and global visual cues.Abstract:
Although mobile robots are increasingly being used in real-world applications, the ability to robustly sense and interact with the environment is still missing. A key requirement for the widespread deployment of mobile robots is the ability to operate autonomously by learning desired environmental models and revising the learned models in response to environmental changes. This paper presents an approach that enables a mobile robot to autonomously learn layered models for environmental objects using temporal, local and global visual cues. A temporal assessment of image gradient features is used to detect candidate objects, which are then modeled using color distribution statistics and a spatial representation of gradient features. The robot incrementally revises the learned models and uses them for object recognition and tracking based on a matching scheme comprising a spatial similarity measure and second order distribution statistics. All algorithms are implemented and tested on a wheeled robot platform in dynamic indoor environments.read more
Citations
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Patent
Simultaneous localization and mapping using spatial and temporal coherence for indoor location
Shao-Wen Yang,Xue Yang,Lei Yang +2 more
TL;DR: In this article, one or more systems, apparatuses, methods, etc. for detecting precise indoor location of a portable wireless device based on a WiFi simultaneous localization and mapping (SLAM) algorithm that implements spatial and temporal coherence.
Proceedings ArticleDOI
ASP+POMDP: Integrating non-monotonic logic programming and probabilistic planning on robots
TL;DR: Answer Set Programming (ASP), a non-monotonic logic programming paradigm, is used to represent, reason with and revise domain knowledge obtained from sensor inputs and high-level human feedback, while hierarchical partially observable Markov decision processes (POMDPs) are used to automatically adapt visual sensing and information processing to the task at hand.
Proceedings ArticleDOI
Move and the robot will learn: Vision-based autonomous learning of object models
Xiang Li,Mohan Sridharan +1 more
TL;DR: The algorithm enables robots to use motion cues to identify and focus on a set of interesting objects, automatically extracting appearance-based and contextual cues from a small number of images to efficiently learn representative models of these objects.
Book ChapterDOI
An Integrated Framework for Robust Human-Robot Interaction
TL;DR: This chapter describes an integrated framework that jointly addresses the learning, adaptation, and interaction challenges associated with robust human-robot interaction in real-world application domains.
Learning Visual Object Models on a Robot Using Context and Appearance Cues (Extended Abstract)
TL;DR: In this paper, the authors used appearance-based and contextual cues extracted from a small number of images to efficiently learn models of these objects, which are used for object recognition in novel scenes based on energy minimization and a generative model for information fusion.
References
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TL;DR: The high utility of MSERs, multiple measurement regions and the robust metric is demonstrated in wide-baseline experiments on image pairs from both indoor and outdoor scenes.