Probabilistic Semantic Data Association for Collaborative Human-Robot Sensing

Approved: Yes
Title: Probabilistic Semantic Data Association for Collaborative Human-Robot Sensing
Category: Docs
Skill Level:
Advanced (306)

Description:
Humans cannot always be treated as oracles for collaborative sensing. Robots
thus need to maintain beliefs over unknown world states when receiving
semantic data from humans, as well as account for possible discrepancies
between human-provided data and these beliefs. To this end, this paper
introduces the problem of semantic data association (SDA) in relation to
conventional data association problems for sensor fusion. It then, develops a
novel probabilistic semantic data association (PSDA) algorithm to rigorously
address SDA in general settings. Simulations of a multi-object search task
show that PSDA enables robust collaborative state estimation under a wide
range of conditions.


Link to Resource:
- Probabilistic Semantic Data Association for Collaborative Human-Robot Sensing (https://arxiv.org/abs/2110.09621)

Tags:
ai (271), machine-learning (272)

Domain:
ACCESS CSSN (780), Campus Champions (572), CAREERS (323), CCMNet (835), Great Plains (311), Kentucky (322), Northeast (308)

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