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Title: Multi-Class Target Tracking Using the Semantic PHD Filter
In order for a mobile robot to be able to effectively operate in complex, dynamic environments it must be capable of understanding both where and what the objects around them are. In this paper we introduce the semantic probability hypothesis density (SPHD) filter, which allows robots to simultaneously track multiple classes of targets despite measurement uncertainty, including false positive detections, false negative detections, measurement noise, and target misclassification. The SPHD filter is capable of incorporating a different motion model for each type of target and of functioning in situations where the number of targets is unknown and time-varying. We demonstrate the efficacy of the SPHD filter via simulations with multiple target types containing both static and dynamic targets. We show that the SPHD filter performs better than a collection of PHD filters running in parallel, one for each target class.  more » « less
Award ID(s):
1830419
NSF-PAR ID:
10119170
Author(s) / Creator(s):
;
Date Published:
Journal Name:
International Symposium on Robotics Research
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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