Incorporating Spatial Constraints into a Bayesian Tracking Framework for Improved Localisation in Agricultural Environments

Khan, Waqas, Das, Gautham, Hanheide, Marc and Cielniak, Grzegorz (2020) Incorporating Spatial Constraints into a Bayesian Tracking Framework for Improved Localisation in Agricultural Environments. In: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, Las Vegas, NV, USA.

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Incorporating Spatial Constraints into a Bayesian Tracking Framework for Improved Localisation in Agricultural Environments
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Abstract

Global navigation satellite system (GNSS) has been considered as a panacea for positioning and tracking since the last decade. However, it suffers from severe limitations in terms of accuracy, particularly in highly cluttered and indoor environments. Though real-time kinematics (RTK) supported GNSS promises extremely accurate localisation, employing such services are expensive, fail in occluded environments and are unavailable in areas where cellular base stations are not accessible. It is, therefore, necessary that the GNSS data is to be filtered if high accuracy is required. Thus, this article presents a GNSS-based particle filter that exploits the spatial constraints imposed by the environment. In the proposed setup, the state prediction of the sample set follows a restricted motion according to the topological map of the environment. This results in the transition of the samples getting confined between specific discrete points, called the topological nodes, defined by a topological map. This is followed by a refinement stage where the full set of predicted samples goes through weighting and resampling, where the weight is proportional to the predicted particle’s proximity with the GNSS measurement. Thus, a discrete space continuous-time Bayesian filter is proposed, called the Topological Particle Filter (TPF).
The proposed TPF is put to test by localising and tracking fruit pickers inside polytunnels. Fruit pickers inside polytunnels can only follow specific paths according to the topology of the tunnel. These paths are defined in the topological map of the polytunnels and are fed to TPF to tracks fruit pickers. Extensive datasets are collected to demonstrate the improved discrete tracking of strawberry pickers inside polytunnels thanks to the exploitation of the environmental constraints.

Keywords:human localisation, Human tracking, Particle Filters, topological mapping
Subjects:H Engineering > H670 Robotics and Cybernetics
Divisions:College of Science > Lincoln Institute for Agri-Food Technology
ID Code:42419
Deposited On:23 Sep 2020 08:59

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