Robust Estimation Framework with Semantic Measurements

Title	Robust Estimation Framework with Semantic Measurements
Authors	Karena X. Cai, Alexei Harvard, Richard M. Murray and Soon-Jo Chung
Source	Submitted, 2019 American Control Conference (ACC)
Abstract	Conventional simultaneous localization and map- ping (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 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 con- fusion 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. Also, the certainty metric of object detection measurements derived from the HMM in our simulation are greater than the certainty levels provided by the confusion matrix in object classification algorithms.
Type	Conference paper
URL	http://www.cds.caltech.edu/~murray/preprints/chmc19-acc s.pdf
DOI
Tag	chmc19-acc
ID	2018d
Funding
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