TY - GEN
T1 - Fast, accurate Gaussian process occupancy maps via test-data octrees and nested Bayesian fusion
AU - Wang, Jinkun
AU - Englot, Brendan
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/6/8
Y1 - 2016/6/8
N2 - We present a novel algorithm to produce descriptive online 3D occupancy maps using Gaussian processes (GPs). GP regression and classification have met with recent success in their application to robot mapping, as GPs are capable of expressing rich correlation among map cells and sensor data. However, the cubic computational complexity has limited its application to large-scale mapping and online use. In this paper we address this issue first by proposing test-data octrees, octrees within blocks of the map that prune away nodes of the same state, condensing the number of test data used in a regression, in addition to allowing fast data retrieval. We also propose a nested Bayesian committee machine which, after new sensor data is partitioned among several GP regressions, fuses the result and updates the map with greatly reduced complexity. Finally, by adjusting the range of influence of the training data and tuning a variance threshold implemented in our method's binary classification step, we are able to control the richness of inference achieved by GPs - and its tradeoff with classification accuracy. The performance of the proposed approach is evaluated with both simulated and real data, demonstrating that the method may serve both as an improved-accuracy classifier, and as a predictive tool to support autonomous navigation.
AB - We present a novel algorithm to produce descriptive online 3D occupancy maps using Gaussian processes (GPs). GP regression and classification have met with recent success in their application to robot mapping, as GPs are capable of expressing rich correlation among map cells and sensor data. However, the cubic computational complexity has limited its application to large-scale mapping and online use. In this paper we address this issue first by proposing test-data octrees, octrees within blocks of the map that prune away nodes of the same state, condensing the number of test data used in a regression, in addition to allowing fast data retrieval. We also propose a nested Bayesian committee machine which, after new sensor data is partitioned among several GP regressions, fuses the result and updates the map with greatly reduced complexity. Finally, by adjusting the range of influence of the training data and tuning a variance threshold implemented in our method's binary classification step, we are able to control the richness of inference achieved by GPs - and its tradeoff with classification accuracy. The performance of the proposed approach is evaluated with both simulated and real data, demonstrating that the method may serve both as an improved-accuracy classifier, and as a predictive tool to support autonomous navigation.
UR - http://www.scopus.com/inward/record.url?scp=84977538798&partnerID=8YFLogxK
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U2 - 10.1109/ICRA.2016.7487232
DO - 10.1109/ICRA.2016.7487232
M3 - Conference contribution
AN - SCOPUS:84977538798
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 1003
EP - 1010
BT - 2016 IEEE International Conference on Robotics and Automation, ICRA 2016
T2 - 2016 IEEE International Conference on Robotics and Automation, ICRA 2016
Y2 - 16 May 2016 through 21 May 2016
ER -