Combined Vision and Frontier-Based Exploration Strategies for Semantic Mapping

2016 IEEE International Conference on Robotics and Automation (ICRA)

Goudou

2016

Self Cite

Searching for objects in an indoor environment can be drastically improved if a task-specific visual saliency is available. We describe a method to incrementally learn such an object-based visual saliency directly on a robot, using an environment exploration mechanism. We first define saliency based on a geometrical criterion and use this definition to segment salient elements given an attentive but costly and restrictive observation of the environment. These elements are used to train a fast classifier that predicts salient objects given large-scale visual features. In order to get a better and faster learning, we use an exploration strategy based on intrinsic motivation to drive our displacement in order to get relevant observations. Our approach has been tested on a robot in indoor environments as well as on publicly available RGB-D images sequences. We demonstrate that the approach outperforms several state-of-the-art methods in the case of indoor object detection and that the exploration strategy can drastically decrease the time required for learning saliency.

Section: Introductionmentioning

confidence: 99%

Environment exploration for object-based visual saliency learning

2016 IEEE International Conference on Robotics and Automation (ICRA)

Goudou

2016

Self Cite

“…Active exploration by robots can be done in many different ways depending on the task and available hardware. On mobile robots aiming at making a semantic cartography of the environment, predefined path plans [16], navigation graphs [13], or frontier-based explorations [12] can drive the robot's displacement. In the context of learning an optimal action policy given visual inputs (typically learning eye saccades to identify objects), reinforcement learning approaches have been proposed [4], [18].…”

Section: Introductionmentioning

confidence: 99%

RL-IAC: An exploration policy for online saliency learning on an autonomous mobile robot

2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Goudou

2016

Self Cite

In the context of visual object search and localization, saliency maps provide an efficient way to find object candidates in images. Unlike most approaches, we propose a way to learn saliency maps directly on a robot, by exploring the environment, discovering salient objects using geometric cues, and learning their visual aspects. More importantly, we provide an autonomous exploration strategy able to drive the robot for the task of learning saliency. For that, we describe the Reinforcement Learning-Intelligent Adaptive Curiosity algorithm (RL-IAC), a mechanism based on IAC (Intelligent Adaptive Curiosity) able to guide the robot through areas of the space where learning progress is high, while minimizing the time spent to move in its environment without learning. We demonstrate first that our saliency approach is an efficient tool to generate relevant object boxes proposal in the input image and significantly outperforms the state-of-the-art EdgeBoxes algorithm. Second, we show that RL-IAC can drastically decrease the required time for learning saliency compared to random exploration.

“…The exploration problem in mobile robotics is often related with a problem of maximizing map under constrains. For example, minimizing displacement time [51] while visiting a certain number of areas by solving a traveling salesman problem, minimizing the number of views [34] with an art-gallery problem, or re-visiting previously observed areas based on potential uncertainty reduction [37] or information-gain [62]. Unlike visual attention strategies, exploration based on map coverage is often composed of a problem of displacement cost minimization.…”

Section: B Autonomous Environment Explorationmentioning

confidence: 99%

Exploring to learn visual saliency: The RL-IAC approach

Lesort

Robotics and Autonomous Systems

et al. 2019

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The problem of object localization and recognition on autonomous mobile robots is still an active topic. In this context, we tackle the problem of learning a model of visual saliency directly on a robot. This model, learned and improved on-the-fly during the robot's exploration provides an efficient tool for localizing relevant objects within their environment. The proposed approach includes two intertwined components. On the one hand, we describe a method for learning and incrementally updating a model of visual saliency from a depth-based object detector. This model of saliency can also be exploited to produce bounding box proposals around objects of interest. On the other hand, we investigate an autonomous exploration technique to efficiently learn such a saliency model. The proposed exploration, called Reinforcement Learning-Intelligent Adaptive Curiosity (RL-IAC) is able to drive the robot's exploration so that samples selected by the robot are likely to improve the current model of saliency. We then demonstrate that such a saliency model learned directly on a robot outperforms several state-of-the-art saliency techniques, and that RL-IAC can drastically decrease the required time for learning a reliable saliency model.