| 1. |
- 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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| 2. |
- Bore, Nils, et al.
(författare)
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Detection and Tracking of General Movable Objects in Large Three-Dimensional Maps
- 2019
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Ingår i: IEEE Transactions on robotics. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 1552-3098 .- 1941-0468. ; 35:1, s. 231-247
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Tidskriftsartikel (refereegranskat)abstract
- This paper studies the problem of detection and tracking of general objects with semistatic dynamics observed by a mobile robot moving in a large environment. A key problem is that due to the environment scale, the robot can only observe a subset of the objects at any given time. Since some time passes between observations of objects in different places, the objects might be moved when the robot is not there. We propose a model for this movement in which the objects typically only move locally, but with some small probability they jump longer distances through what we call global motion. For filtering, we decompose the posterior over local and global movements into two linked processes. The posterior over the global movements and measurement associations is sampled, while we track the local movement analytically using Kalman filters. This novel filter is evaluated on point cloud data gathered autonomously by a mobile robot over an extended period of time. We show that tracking jumping objects is feasible, and that the proposed probabilistic treatment outperforms previous methods when applied to real world data. The key to efficient probabilistic tracking in this scenario is focused sampling of the object posteriors.
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| 3. |
- Ekekrantz, Johan, et al.
(författare)
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Adaptive Iterative Closest Keypoint
- 2013
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Ingår i: 2013 European Conference on Mobile Robots, ECMR 2013 - Conference Proceedings. - New York : IEEE. - 9781479902637 ; , s. 80-87
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Konferensbidrag (refereegranskat)abstract
- Finding accurate correspondences between overlapping 3D views is crucial for many robotic applications, from multi-view 3D object recognition to SLAM. This step, often referred to as view registration, plays a key role in determining the overall system performance. In this paper, we propose a fast and simple method for registering RGB-D data, building on the principle of the Iterative Closest Point (ICP) algorithm. In contrast to ICP, our method exploits both point position and visual appearance and is able to smoothly transition the weighting between them with an adaptive metric. This results in robust initial registration based on appearance and accurate final registration using 3D points. Using keypoint clustering we are able to utilize a non exhaustive search strategy, reducing runtime of the algorithm significantly. We show through an evaluation on an established benchmark that the method significantly outperforms current methods in both robustness and precision.
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| 4. |
- Ekekrantz, Johan, et al.
(författare)
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Probabilistic Primitive Refinement algorithm for colored point cloud data
- 2015
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Ingår i: 2015 European Conference on Mobile Robots (ECMR). - Lincoln : IEEE conference proceedings.
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Konferensbidrag (refereegranskat)abstract
- In this work we present the Probabilistic Primitive Refinement (PPR) algorithm, an iterative method for accurately determining the inliers of an estimated primitive (such as planes and spheres) parametrization in an unorganized, noisy point cloud. The measurement noise of the points belonging to the proposed primitive surface are modelled using a Gaussian distribution and the measurements of extraneous points to the proposed surface are modelled as a histogram. Given these models, the probability that a measurement originated from the proposed surface model can be computed. Our novel technique to model the noisy surface from the measurement data does not require a priori given parameters for the sensor noise model. The absence of sensitive parameters selection is a strength of our method. Using the geometric information obtained from such an estimate the algorithm then builds a color-based model for the surface, further boosting the accuracy of the segmentation. If used iteratively the PPR algorithm can be seen as a variation of the popular mean-shift algorithm with an adaptive stochastic kernel function.
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| 5. |
- Krajnik, T., et al.
(författare)
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Long-term topological localisation for service robots in dynamic environments using spectral maps
- 2014
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Ingår i: IEEE International Conference on Intelligent Robots and Systems. Proceedings. - : IEEE Press. - 2153-0858 .- 2153-0866. - 9781479969340 ; , s. 4537-4542
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents a new approach for topological localisation of service robots in dynamic indoor environments. In contrast to typical localisation approaches that rely mainly on static parts of the environment, our approach makes explicit use of information about changes by learning and modelling the spatio-temporal dynamics of the environment where the robot is acting. The proposed spatio-temporal world model is able to predict environmental changes in time, allowing the robot to improve its localisation capabilities during long-term operations in populated environments. To investigate the proposed approach, we have enabled a mobile robot to autonomously patrol a populated environment over a period of one week while building the proposed model representation. We demonstrate that the experience learned during one week is applicable for topological localization even after a hiatus of three months by showing that the localization error rate is significantly lower compared to static environment representations.
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| 6. |
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