This paper analyzes algorithms and sensor fusion architectures used to mechanize a self-contained, pose-estimation for the feet of humans or humanoid robots. The approaches makes use of a network of low-cost, inertial measurement units (IMUs) affixed to the feet. By leveraging known equality and inequality constraints between the motion and location of the IMUs, drift due to inertial sensor output errors are reduced or eliminated. Two sensor fusion approaches are evaluated; a de-centralized estimator and centralized estimator. Experimental results demonstrating the performance of these fusion schemes are presented. Issues associated with tuning the de-centralized and centralized estimators are discussed in detail.
|Original language||English (US)|
|Title of host publication||Proceedings of the IEEE/ION Position, Location and Navigation Symposium, PLANS 2016|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||9|
|State||Published - May 26 2016|
|Event||IEEE/ION Position, Location and Navigation Symposium, PLANS 2016 - Savannah, Georgia|
Duration: Apr 11 2016 → Apr 14 2016
|Name||Proceedings of the IEEE/ION Position, Location and Navigation Symposium, PLANS 2016|
|Other||IEEE/ION Position, Location and Navigation Symposium, PLANS 2016|
|Period||4/11/16 → 4/14/16|
Bibliographical noteFunding Information:
This material is based upon work supported by the National Science Foundation under Grant No. 1328722. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.
- Personal navigation
- constrained filtering
- inertial navigation