Active Random Hypersurface Models: Simultaneous Shape and Pose Tracking of Extended Objects in Noisy Point Clouds
Final Report Abstract
In this project, we proposed and developed an approach to estimate extended target objects based on point measurements. The result was called Active Random Hypersurface Models, which extends the modeling techniques of Random Hypersurface Models with ideas drawn from Active Contour Models. The key idea was to combine state-of-the-art association models based on probabilistic association and distance-based minimization, in order to construct complex target shapes from simple forms using extrusions, symmetries, compositions, and other transformations. In this context, we planned to solve three challenges. First, the resulting estimator needed to handle different uncertainties specific to each measurement, which could change quickly in time. Second, the shape models needed to be flexible, allowing for low details in cases of low measurement quality, and high accuracy when more information is available. Finally, it should be able to deal gracefully with low prior information. In particular, it should allow for the estimation of shape and pose simultaneously. The project had to undergo significant restructuring due to the fact that its work packages were planned for two researchers, but the final grant provided funds for only one researcher. Because of this, we planned five blocks that encompassed about half of the tasks of the proposed work packages. The first block was concerned with the sensor model. Due to the modified time constraints, a new sensor framework could not be developed, and the models used were based heavily on previous work. Instead, we focused on exploiting all the available sensor information by incorporating negative measurements, which told us where the target cannot be, in addition to the traditional positive observations. For the second block, we dealt with modeling simple, two-dimensional shapes. To achieve this, we proposed a bias-reduction approach that allows for shape estimation even in high noise environments. The third block was concerned with describing arbitrary, non-convex shapes, an issue which was addressed with the introduction of Level-set ARHMs. For this approach, the idea was to describe the interior of a shape through level-sets of a distance function, and to represent the boundary by polygons subject to regularization. The fourth block developed a technique to translate the previously explored concepts into three dimensions, with the use of extrusions and symmetries. Finally, a mechanism for composition, that is, describing a shape as the union of multiple components joined together, was explored in the fifth block. Overall, the main result of the project was a recursive Bayesian estimator for tracking a wide variety of extended objects, which can be used in both particle filters and nonlinear Kalman filters, and easily allows for the fusion of multiple types of measurements. This estimator can find applications in a broad spectrum of fields, such as robotics, indoor navigation, and augmented reality. We also showed two possible direct applications on projects on which the ISAS laboratory is participating. While not all work packages could be implemented, they can still benefit directly from the work presented by this project, and can be implemented as part of a future grant.
Publications
- Tracking Connected Objects Using Interacting Shape Models. In Proceedings of the 17th International Conference on Information Fusion (Fusion 2014), Salamanca, Spain, July 2014
Zea, Antonio, Florian Faion and Uwe D. Hanebeck
- Tracking Extended Objects using Extrusion Random Hypersurface Models. In Proceedings of the IEEE ISIF Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF 2014), Bonn, Germany, October 2014
Zea, Antonio, Florian Faion and Uwe D. Hanebeck
(See online at https://dx.doi.org/10.1109/SDF.2014.6954722) - Shape Tracking using Partial Information Models. In Proceedings of the 2015 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2015), San Diego, California, USA, September 2015
Zea, Antonio, Florian Faion and Uwe D. Hanebeck
(See online at https://dx.doi.org/10.1109/MFI.2015.7295736) - Symmetries in Bayesian Extended Object Tracking. Journal of Advances in Information Fusion, June 2015
Faion, Florian, Antonio Zea, Marcus Baum and Uwe D. Hanebeck
- Tracking Elongated Extended Objects Using Splines. In Proceedings of the 19th International Conference on Information Fusion (Fusion 2016), Heidelberg, Germany, July 2016
Zea, Antonio, Florian Faion and Uwe D. Hanebeck
- Level- Set Random Hyper Surface Models for Tracking Non-Convex Extended Objects. IEEE Transactions on Aerospace and Electronic Systems, 2017
Zea, Antonio, Florian Faion, Marcus Baum and Uwe D. Hanebeck
(See online at https://dx.doi.org/10.1109/TAES.2016.130704)
