| 1. |
- Alberti, Marina, et al.
(författare)
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Relational approaches for joint object classification andscene similarity measurement in indoor environments
- 2014
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Ingår i: Proc. of 2014 AAAI Spring Symposium QualitativeRepresentations for Robots 2014. - Palo Alto, California : The AAAI Press.
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Konferensbidrag (refereegranskat)abstract
- The qualitative structure of objects and their spatial distribution,to a large extent, define an indoor human environmentscene. This paper presents an approach forindoor scene similarity measurement based on the spatialcharacteristics and arrangement of the objects inthe scene. For this purpose, two main sets of spatialfeatures are computed, from single objects and objectpairs. A Gaussian Mixture Model is applied both onthe single object features and the object pair features, tolearn object class models and relationships of the objectpairs, respectively. Given an unknown scene, the objectclasses are predicted using the probabilistic frameworkon the learned object class models. From the predictedobject classes, object pair features are extracted. A fi-nal scene similarity score is obtained using the learnedprobabilistic models of object pair relationships. Ourmethod is tested on a real world 3D database of deskscenes, using a leave-one-out cross-validation framework.To evaluate the effect of varying conditions on thescene similarity score, we apply our method on mockscenes, generated by removing objects of different categoriesin the test scenes.
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| 2. |
- Almeida, Diogo, 1991-, et al.
(författare)
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Team KTH’s Picking Solution for the Amazon Picking Challenge 2016
- 2017
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Ingår i: Warehouse Picking Automation Workshop 2017.
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Konferensbidrag (populärvet., debatt m.m.)abstract
- In this work we summarize the solution developed by Team KTH for the Amazon Picking Challenge 2016 in Leipzig, Germany. The competition simulated a warehouse automation scenario and it was divided in two tasks: a picking task where a robot picks items from a shelf and places them in a tote and a stowing task which is the inverse task where the robot picks items from a tote and places them in a shelf. We describe our approach to the problem starting from a high level overview of our system and later delving into details of our perception pipeline and our strategy for manipulation and grasping. The solution was implemented using a Baxter robot equipped with additional sensors.
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| 3. |
- Almeida, Diogo, 1991-, et al.
(författare)
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Team KTH’s Picking Solution for the Amazon Picking Challenge 2016
- 2020
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Ingår i: Advances on Robotic Item Picking: Applications in Warehousing and E-Commerce Fulfillment. - Cham : Springer Nature. ; , s. 53-62
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- In this chapter we summarize the solution developed by team KTH for the Amazon Picking Challenge 2016 in Leipzig, Germany. The competition, which simulated a warehouse automation scenario, was divided into two parts: a picking task, where the robot picks items from a shelf and places them into a tote, and a stowing task, where the robot picks items from a tote and places them in a shelf. We describe our approach to the problem starting with a high-level overview of the system, delving later into the details of our perception pipeline and strategy for manipulation and grasping. The hardware platform used in our solution consists of a Baxter robot equipped with multiple vision sensors.
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| 4. |
- Ambrus, Rares, et al.
(författare)
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Autonomous meshing, texturing and recognition of object models with a mobile robot
- 2017
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Ingår i: 2017 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS). - Vancouver, Canada : IEEE. - 9781538626825 ; , s. 5071-5078
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Konferensbidrag (refereegranskat)abstract
- We present a system for creating object models from RGB-D views acquired autonomously by a mobile robot. We create high-quality textured meshes of the objects by approximating the underlying geometry with a Poisson surface. Our system employs two optimization steps, first registering the views spatially based on image features, and second aligning the RGB images to maximize photometric consistency with respect to the reconstructed mesh. We show that the resulting models can be used robustly for recognition by training a Convolutional Neural Network (CNN) on images rendered from the reconstructed meshes. We perform experiments on data collected autonomously by a mobile robot both in controlled and uncontrolled scenarios. We compare quantitatively and qualitatively to previous work to validate our approach.
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| 5. |
- Ambrus, Rares, et al.
(författare)
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Meta-rooms : Building and Maintaining Long Term Spatial Models in a Dynamic World
- 2014
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Ingår i: 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, (IROS 2014). - : IEEE conference proceedings. - 9781479969340 ; , s. 1854-1861
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Konferensbidrag (refereegranskat)abstract
- We present a novel method for re-creating the static structure of cluttered office environments -which we define as the " meta-room" -from multiple observations collected by an autonomous robot equipped with an RGB-D depth camera over extended periods of time. Our method works directly with point clusters by identifying what has changed from one observation to the next, removing the dynamic elements and at the same time adding previously occluded objects to reconstruct the underlying static structure as accurately as possible. The process of constructing the meta-rooms is iterative and it is designed to incorporate new data as it becomes available, as well as to be robust to environment changes. The latest estimate of the meta-room is used to differentiate and extract clusters of dynamic objects from observations. In addition, we present a method for re-identifying the extracted dynamic objects across observations thus mapping their spatial behaviour over extended periods of time.
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| 6. |
- Ambrus, Rares
(författare)
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Unsupervised construction of 4D semantic maps in a long-term autonomy scenario
- 2017
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Robots are operating for longer times and collecting much more data than just a few years ago. In this setting we are interested in exploring ways of modeling the environment, segmenting out areas of interest and keeping track of the segmentations over time, with the purpose of building 4D models (i.e. space and time) of the relevant parts of the environment.Our approach relies on repeatedly observing the environment and creating local maps at specific locations. The first question we address is how to choose where to build these local maps. Traditionally, an operator defines a set of waypoints on a pre-built map of the environment which the robot visits autonomously. Instead, we propose a method to automatically extract semantically meaningful regions from a point cloud representation of the environment. The resulting segmentation is purely geometric, and in the context of mobile robots operating in human environments, the semantic label associated with each segment (i.e. kitchen, office) can be of interest for a variety of applications. We therefore also look at how to obtain per-pixel semantic labels given the geometric segmentation, by fusing probabilistic distributions over scene and object types in a Conditional Random Field.For most robotic systems, the elements of interest in the environment are the ones which exhibit some dynamic properties (such as people, chairs, cups, etc.), and the ability to detect and segment such elements provides a very useful initial segmentation of the scene. We propose a method to iteratively build a static map from observations of the same scene acquired at different points in time. Dynamic elements are obtained by computing the difference between the static map and new observations. We address the problem of clustering together dynamic elements which correspond to the same physical object, observed at different points in time and in significantly different circumstances. To address some of the inherent limitations in the sensors used, we autonomously plan, navigate around and obtain additional views of the segmented dynamic elements. We look at methods of fusing the additional data and we show that both a combined point cloud model and a fused mesh representation can be used to more robustly recognize the dynamic object in future observations. In the case of the mesh representation, we also show how a Convolutional Neural Network can be trained for recognition by using mesh renderings.Finally, we present a number of methods to analyse the data acquired by the mobile robot autonomously and over extended time periods. First, we look at how the dynamic segmentations can be used to derive a probabilistic prior which can be used in the mapping process to further improve and reinforce the segmentation accuracy. We also investigate how to leverage spatial-temporal constraints in order to cluster dynamic elements observed at different points in time and under different circumstances. We show that by making a few simple assumptions we can increase the clustering accuracy even when the object appearance varies significantly between observations. The result of the clustering is a spatial-temporal footprint of the dynamic object, defining an area where the object is likely to be observed spatially as well as a set of time stamps corresponding to when the object was previously observed. Using this data, predictive models can be created and used to infer future times when the object is more likely to be observed. In an object search scenario, this model can be used to decrease the search time when looking for specific objects.
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| 7. |
- Ambrus, Rares, et al.
(författare)
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Unsupervised learning of spatial-temporal models of objects in a long-term autonomy scenario
- 2015
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Ingår i: 2015 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS). - : IEEE. - 9781479999941 ; , s. 5678-5685
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Konferensbidrag (refereegranskat)abstract
- We present a novel method for clustering segmented dynamic parts of indoor RGB-D scenes across repeated observations by performing an analysis of their spatial-temporal distributions. We segment areas of interest in the scene using scene differencing for change detection. We extend the Meta-Room method and evaluate the performance on a complex dataset acquired autonomously by a mobile robot over a period of 30 days. We use an initial clustering method to group the segmented parts based on appearance and shape, and we further combine the clusters we obtain by analyzing their spatial-temporal behaviors. We show that using the spatial-temporal information further increases the matching accuracy.
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| 8. |
- Ambrus, Rares, et al.
(författare)
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Unsupervised object segmentation through change detection in a long term autonomy scenario
- 2016
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Ingår i: IEEE-RAS International Conference on Humanoid Robots. - : IEEE. - 9781509047185 ; , s. 1181-1187
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Konferensbidrag (refereegranskat)abstract
- In this work we address the problem of dynamic object segmentation in office environments. We make no prior assumptions on what is dynamic and static, and our reasoning is based on change detection between sparse and non-uniform observations of the scene. We model the static part of the environment, and we focus on improving the accuracy and quality of the segmented dynamic objects over long periods of time. We address the issue of adapting the static structure over time and incorporating new elements, for which we train and use a classifier whose output gives an indication of the dynamic nature of the segmented elements. We show that the proposed algorithms improve the accuracy and the rate of detection of dynamic objects by comparing with a labelled dataset.
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| 9. |
- Andersson, Olov, 1979-
(författare)
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Learning to Make Safe Real-Time Decisions Under Uncertainty for Autonomous Robots
- 2020
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Robots are increasingly expected to go beyond controlled environments in laboratories and factories, to act autonomously in real-world workplaces and public spaces. Autonomous robots navigating the real world have to contend with a great deal of uncertainty, which poses additional challenges. Uncertainty in the real world accrues from several sources. Some of it may originate from imperfect internal models of reality. Other uncertainty is inherent, a direct side effect of partial observability induced by sensor limitations and occlusions. Regardless of the source, the resulting decision problem is unfortunately computationally intractable under uncertainty. This poses a great challenge as the real world is also dynamic. It will not pause while the robot computes a solution. Autonomous robots navigating among people, for example in traffic, need to be able to make split-second decisions. Uncertainty is therefore often neglected in practice, with potentially catastrophic consequences when something unexpected happens. The aim of this thesis is to leverage recent advances in machine learning to compute safe real-time approximations to decision-making under uncertainty for real-world robots. We explore a range of methods, from probabilistic to deep learning, as well as different combinations with optimization-based methods from robotics, planning and control. Driven by applications in robot navigation, and grounded in experiments with real autonomous quadcopters, we address several parts of this problem. From reducing uncertainty by learning better models, to directly approximating the decision problem itself, all the while attempting to satisfy both the safety and real-time requirements of real-world autonomy.
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| 10. |
- Autin, D, et al.
(författare)
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Using multiple gaussian hypotheses to represent probability distributions for mobile robot localization
- 2000
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Konferensbidrag (refereegranskat)abstract
- A new mobile robot localization technique is presented which uses multiple Gaussian hypotheses to represent the probability distribution of the robots location in the environment. A tree of hypotheses is built by the application of Bayes' rule with each new sensor mesurement. However, such a tree can grow without bound and so rules are introduced for the elimination of the least likely hypotheses from the tree and for the proper re-distribution of their probability. This technique is applied to a feature-based mobile robot localization scheme and experimental results are given demonstrating the effectiveness of the scheme.
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