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Robust Estimation Framework with Semantic Measurements

Cai, Karena X. and Harvard, Alexei and Murray, Richard M. and Chung, Soon-Jo (2019) Robust Estimation Framework with Semantic Measurements. In: 2019 American Control Conference (ACC). IEEE , Piscataway, NJ, pp. 3809-3816. ISBN 978-1-5386-7926-5. https://resolver.caltech.edu/CaltechAUTHORS:20190426-090024789

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Abstract

Conventional simultaneous localization and mapping (SLAM) algorithms rely on geometric measurements and require loop-closure detections to correct for drift accumulated over a vehicle trajectory. Semantic measurements can add measurement redundancy and provide an alternative form of loop closure. We propose two different estimation algorithms that incorporate semantic measurements provided by vision-based object classifiers. An a priori map of regions where the objects can be detected is assumed. The first estimation framework is posed as a maximum-likelihood problem, where the likelihood function for semantic measurements is derived from the confusion matrices of the object classifiers. The second estimation framework is comprised of two parts: 1) a continuous-state estimation formulation that includes semantic measurements as a form of state constraints and 2) a discrete-state estimation formulation used to compute the certainty of object detection measurements using a Hidden Markov Model (HMM). The advantages of incorporating semantic measurements in these frameworks are demonstrated in numerical simulations. In particular, the proposed estimation algorithms improve upon the robustness and accuracy of conventional SLAM algorithms.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
https://doi.org/10.23919/ACC.2019.8814793DOIArticle
https://ieeexplore.ieee.org/document/8814793PublisherArticle
ORCID:
AuthorORCID
Murray, Richard M.0000-0002-5785-7481
Chung, Soon-Jo0000-0002-6657-3907
Additional Information:© 2019 AACC. This work was in part supported by AeroVironment, Inc., Boeing, and Caltech’s Center for Autonomous Systems and Technologies (CAST). The authors would like to acknowledge Andrew Stuart for helpful conversations on data assimilation methods for this paper.
Group:GALCIT, Center for Autonomous Systems and Technologies (CAST)
Funders:
Funding AgencyGrant Number
AeroVironmentUNSPECIFIED
Boeing CorporationUNSPECIFIED
Center for Autonomous Systems and TechnologiesUNSPECIFIED
Record Number:CaltechAUTHORS:20190426-090024789
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20190426-090024789
Official Citation:K. X. Cai, A. Harvard, R. M. Murray and S. Chung, "Robust Estimation Framework with Semantic Measurements," 2019 American Control Conference (ACC), Philadelphia, PA, USA, 2019, pp. 3809-3816. doi: 10.23919/ACC.2019.8814793
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:95012
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:26 Apr 2019 16:12
Last Modified:15 Nov 2019 18:32

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