TY - GEN
T1 - Data Association Aware Semantic Mapping and Localization via a Viewpoint-Dependent Classifier Model
AU - Tchuiev, Vladimir
AU - Feldman, Yuri
AU - Vadim, Indelman
N1 - Funding Information: V. Tchuiev and V. Indelman are with the Department of Aerospace Engineering, Technion - Israel Institute of Technology, Haifa 32000, Israel. Y. Feldman is with the Department of Computer Science, Technion - Israel Institute of Technology, Haifa 32000, Israel. {vovatch, vadim.indelman}@technion.ac.il, [email protected]. This work was partially supported by the Israel Ministry of Science & Technology (MOST). Publisher Copyright: © 2019 IEEE.
PY - 2019/11
Y1 - 2019/11
N2 - We present an approach for localization and semantic mapping in ambiguous scenarios by incrementally maintaining a hybrid belief over continuous states and discrete classification and data association variables. Unlike existing incremental approaches, we explicitly maintain data association components over time, which allows us to deal with perceptual aliasing. Crucially, we utilize a viewpoint-dependent classifier model over rich classifier outputs and leverage the coupling between poses and semantic measurements both for disambiguating data association and in pose estimation. We demonstrate in simulation that incorporating semantic measurements with a viewpoint-dependent classifier model enhances disambiguation of both data association and localization over usage of only geometric measurements or viewpoint independent models, further contributing to the tractability of the approach in practice, and providing better estimates.
AB - We present an approach for localization and semantic mapping in ambiguous scenarios by incrementally maintaining a hybrid belief over continuous states and discrete classification and data association variables. Unlike existing incremental approaches, we explicitly maintain data association components over time, which allows us to deal with perceptual aliasing. Crucially, we utilize a viewpoint-dependent classifier model over rich classifier outputs and leverage the coupling between poses and semantic measurements both for disambiguating data association and in pose estimation. We demonstrate in simulation that incorporating semantic measurements with a viewpoint-dependent classifier model enhances disambiguation of both data association and localization over usage of only geometric measurements or viewpoint independent models, further contributing to the tractability of the approach in practice, and providing better estimates.
UR - https://www.scopus.com/pages/publications/85081158746
U2 - 10.1109/IROS40897.2019.8967855
DO - 10.1109/IROS40897.2019.8967855
M3 - Conference contribution
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 7742
EP - 7749
BT - 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019
T2 - 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019
Y2 - 3 November 2019 through 8 November 2019
ER -