Not seeing is also believing: Combining object and metric spatial information

Wong, Lawson L. S.; Kaelbling, Leslie Pack; Lozano-Pérez, Tomás

doi:10.1109/icra.2014.6907014

Cited by 10 publications

(8 citation statements)

References 16 publications

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“…As a result, the algorithm automatically adjusts the control synthesis for multiple objects in an environment. Notably, ergodic exploration uses non-contact motion data (sensor motion not in contact with an object) [27], [28] to improve the shape estimate. The idea of utilizing free space is often found in other related works of pose estimation and tracking [27]- [30] and is emphasized in our work.…”

Section: Introductionmentioning

confidence: 99%

“…Notably, ergodic exploration uses non-contact motion data (sensor motion not in contact with an object) [27], [28] to improve the shape estimate. The idea of utilizing free space is often found in other related works of pose estimation and tracking [27]- [30] and is emphasized in our work. As a final contribution, we show the algorithm is modular with respect to the choice of shape representation and tactile information distribution.…”

Section: Introductionmentioning

confidence: 99%

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Ergodic Exploration Using Binary Sensing for Nonparametric Shape Estimation

Abraham

Prabhakar

Hartmann

et al. 2017

IEEE Robot. Autom. Lett.

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Current methods to estimate object shape—using either vision or touch—generally depend on high-resolution sensing. Here, we exploit ergodic exploration to demonstrate successful shape estimation when using a low-resolution binary contact sensor. The measurement model is posed as a collision-based tactile measurement, and classification methods are used to discriminate between shape boundary regions in the search space. Posterior likelihood estimates of the measurement model help the system actively seek out regions where the binary sensor is most likely to return informative measurements. Results show successful shape estimation of various objects as well as the ability to identify multiple objects in an environment. Interestingly, it is shown that ergodic exploration utilizes non-contact motion to gather significant information about shape. The algorithm is extended in three dimensions in simulation and we present two dimensional experimental results using the Rethink Baxter robot.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Ergodic Exploration Using Binary Sensing for Nonparametric Shape Estimation

Abraham

Prabhakar

Hartmann

et al. 2017

IEEE Robot. Autom. Lett.

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“…To introduce the effect of human presence into the process of 2D space occupancy mapping, our starting point will be the space occupancy posterior introduced by [32]. In their work, they jointly take into account 2D laser and static object pose measurements by:…”

Section: Space Occupancy Predictionmentioning

confidence: 99%

“…The recent work from Wong et al [32] formalizes the probabilistic dependencies between metric measurements and static object poses, as a means to predict object presence attributes from space occupancy and viceversa. By showing the added value in binding metric mapping with static object recognition in constrained examples, that work is incentive to our contribution that advances towards linking human action cues with metric mapping in realistic conditions.…”

Section: Introductionmentioning

confidence: 99%

Binding human spatial interactions with mapping for enhanced mobility in dynamic environments

Papadakis

Rives

2016

Auton Robot

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To cite this version:Panagiotis Papadakis, Patrick Rives. Binding human spatial interactions with mapping for enhanced mobility in dynamic environments. Autonomous Robots, Springer Verlag, 2017, 41 (5) Abstract For mobile robots to operate in compliance with human presence, interpreting the impact of human activities and responding constructively is a challenging goal. In this paper, we propose a generative approach for enhancing robot mapping and mobility in the presence of humans through a joint, probabilistic treatment of static and dynamic characteristics of indoor environments. Human spatial activity is explicitly exploited for the purpose of passage detection and space occupancy prediction while effectively discarding false positive human detections using prior map information. In turn, this allows the execution of plan trajectories within unexplored areas by using human presence for resolving the uncertainty or ambiguity that is due to dynamic events. A series of experiments with an indoor robot navigating in close human proximity within a multifloor building demonstrate the effectiveness of our approach in realistic conditions.

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“…However, it is quite widespread that researchers join topological or geometric maps with data from objects located in the environment. Wong et al, combined a metric map with objects in space [71]. Something similar was proposed by Zhao and Chen [72].…”

mentioning

confidence: 99%

Semantic Information for Robot Navigation: A Survey

et al. 2020

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There is a growing trend in robotics for implementing behavioural mechanisms based on human psychology, such as the processes associated with thinking. Semantic knowledge has opened new paths in robot navigation, allowing a higher level of abstraction in the representation of information. In contrast with the early years, when navigation relied on geometric navigators that interpreted the environment as a series of accessible areas or later developments that led to the use of graph theory, semantic information has moved robot navigation one step further. This work presents a survey on the concepts, methodologies and techniques that allow including semantic information in robot navigation systems. The techniques involved have to deal with a range of tasks from modelling the environment and building a semantic map, to including methods to learn new concepts and the representation of the knowledge acquired, in many cases through interaction with users. As understanding the environment is essential to achieve high-level navigation, this paper reviews techniques for acquisition of semantic information, paying attention to the two main groups: human-assisted and autonomous techniques. Some state-of-the-art semantic knowledge representations are also studied, including ontologies, cognitive maps and semantic maps. All of this leads to a recent concept, semantic navigation, which integrates the previous topics to generate high-level navigation systems able to deal with real-world complex situations.

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Not seeing is also believing: Combining object and metric spatial information

Cited by 10 publications

References 16 publications

Ergodic Exploration Using Binary Sensing for Nonparametric Shape Estimation

Ergodic Exploration Using Binary Sensing for Nonparametric Shape Estimation

Binding human spatial interactions with mapping for enhanced mobility in dynamic environments

Semantic Information for Robot Navigation: A Survey

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