Robust Estimation Framework with Semantic Measurements
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.
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.Attached Files
Published - 08814793.pdf
Submitted - ACC19_1030_FI.pdf
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Additional details
- Eprint ID
- 95012
- Resolver ID
- CaltechAUTHORS:20190426-090024789
- AeroVironment
- Boeing Corporation
- Center for Autonomous Systems and Technologies
- Created
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2019-04-26Created from EPrint's datestamp field
- Updated
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2021-11-16Created from EPrint's last_modified field
- Caltech groups
- GALCIT, Center for Autonomous Systems and Technologies (CAST)