Data Association for Semantic World Modeling from Partial Views

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

doi:10.1007/978-3-319-28872-7_25

Cited by 15 publications

(26 citation statements)

References 18 publications

(9 reference statements)

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“…Our approach is also tightly related to recent work on active hypothesis disambiguation in the context of object detection and classification (Atanasov et al, 2014;Lauri et al, 2015;Patten et al, 2016;Sankaran et al, 2015;Wong et al, 2015). Given hypotheses regarding object class and pose, these approaches aim to find a sequence future viewpoints that will lead to disambiguation, i.e.…”

Section: Related Workmentioning

confidence: 92%

A unified framework for data association aware robust belief space planning and perception

Pathak

Thomas

Indelman

2018

The International Journal of Robotics Research

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We develop a belief space planning approach that advances the state of the art by incorporating reasoning about data association within planning, while considering additional sources of uncertainty. Existing belief space planning approaches typically assume that data association is given and perfect, an assumption that can be harder to justify during operation in the presence of localization uncertainty, or in ambiguous and perceptually aliased environments. By contrast, our data association aware belief space planning (DA-BSP) approach explicitly reasons about data association within belief evolution owing to candidate actions, and as such can better accommodate these challenging real-world scenarios. In particular, we show that, owing to perceptual aliasing, a posterior belief can become a mixture of probability distribution functions and design cost functions, which measure the expected level of ambiguity and posterior uncertainty given candidate action. Furthermore, we also investigate more challenging situations, such as when prior belief is multimodal and when data association aware planning is performed over several look-ahead steps. Our framework models the belief as a Gaussian mixture model. Another unique aspect of this approach is that the number of components of this Gaussian mixture model can increase as well as decrease, thereby reflecting reality more accurately. Using these and standard costs (e.g. control penalty, distance to goal) within the objective function yields a general framework that reliably represents action impact and, in particular, is capable of active disambiguation. Our approach is thus applicable to both robust perception in a passive setting with data given a priori and in an active setting, such as in autonomous navigation in perceptually aliased environments. We demonstrate key aspects of DA-BSP in a theoretical example, in a Gazebo-based realistic simulation, and also on the real robotic platform using a Pioneer robot in an office environment.

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Section: Related Workmentioning

confidence: 92%

A unified framework for data association aware robust belief space planning and perception

Pathak

Thomas

Indelman

2018

The International Journal of Robotics Research

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“…Note that our association method assumes that objects are static and that the localisation and measurement errors are small. In our experiments, the errors were not large enough to cause problems, however, for situations with large error, more robust association methods could be used such as [19].…”

Section: A Data Associationmentioning

confidence: 98%

Viewpoint Evaluation for Online 3-D Active Object Classification

Patten

Zillich

Fitch

et al. 2016

IEEE Robot. Autom. Lett.

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We present an end-to-end method for active object classification in cluttered scenes from RGB-D data. Our algorithms predict the quality of future viewpoints in the form of entropy using both class and pose. Occlusions are explicitly modelled in predicting the visible regions of objects, which modulates the corresponding discriminatory value of a given view. We implement a one-step greedy planner and demonstrate our method online using a mobile robot. We also analyse the performance of our method compared to similar strategies in simulated execution using the Willow Garage dataset. Results show that our active method usefully reduces the number of views required to accurately classify objects in clutter as compared to traditional passive perception.

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“…Consequently, this allowed SLAM systems to additionally include semantically rich information in order to improve their estimation [10]- [12]. Recently, some research specifically addresses the problem of correctly assigning measurements to already known objects utilizing additional semantic information [13], [14]. These systems, however, do not deal with the estimation of the camera's position, i.e.…”

Section: A Related Workmentioning

confidence: 99%

“…Generally, the MHT enables accurate results but is inherently intractable with a large amount of objects and requires frequent optimizations [16]. The work of Wong et al [14] presents an approach using the DPs which yields estimation results comparable to the MHT but with substantially less computational effort. Nevertheless, their proposed approach is not incremental and therefore, not directly applicable for the mapping of a robot's environment.…”

Section: A Related Workmentioning

confidence: 99%

Multiple Hypothesis Semantic Mapping for Robust Data Association

Bernreiter¹,

Gawel²,

Sommer³

et al. 2019

IEEE Robot. Autom. Lett.

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In this paper, we present a semantic mapping approach with multiple hypothesis tracking for data association. As semantic information has the potential to overcome ambiguity in measurements and place recognition, it forms an eminent modality for autonomous systems. This is particularly evident in urban scenarios with several similar looking surroundings. Nevertheless, it requires the handling of a non-Gaussian and discrete random variable coming from object detectors. Previous methods facilitate semantic information for global localization and data association to reduce the instance ambiguity between the landmarks. However, many of these approaches do not deal with the creation of complete globally consistent representations of the environment and typically do not scale well. We utilize multiple hypothesis trees to derive a probabilistic data association for semantic measurements by means of position, instance and class to create a semantic representation. We propose an optimized mapping method and make use of a pose graph to derive a novel semantic SLAM solution. Furthermore, we show that semantic covisibility graphs allow for a precise place recognition in urban environments. We verify our approach using real-world outdoor dataset and demonstrate an average drift reduction of 33 % w.r.t. the raw odometry source. Moreover, our approach produces 55 % less hypotheses on average than a regular multiple hypotheses approach.

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Data Association for Semantic World Modeling from Partial Views

Cited by 15 publications

References 18 publications

A unified framework for data association aware robust belief space planning and perception

A unified framework for data association aware robust belief space planning and perception

Viewpoint Evaluation for Online 3-D Active Object Classification

Multiple Hypothesis Semantic Mapping for Robust Data Association

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