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Many modern simultaneous localization and mapping (SLAM) techniques rely on sparse landmark-based maps due to their real-time performance. However, these techniques frequently assert that these landmarks are fixed in position over time, known as the static-world assumption. This is rarely, if ever, the case in most real-world environments. Even worse, over long deployments, robots are bound to observe traditionally static landmarks change, for example when an autonomous vehicle encounters a construction zone. This work addresses this challenge, accounting for changes in complex three-dimensional environments with the creation of a probabilistic filter that operates on the features that give rise to landmarks. To accomplish this, landmarks are clustered into cliques and a filter is developed to estimate their persistence jointly among observations of the landmarks in a clique. This filter uses estimated spatial-temporal priors of geometric objects, allowing for dynamic and semi-static objects to be removed from a formally static map. The proposed algorithm is validated in a 3D simulated environment.
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Relative target estimation using a cascade of extended Kalman filters
This paper presents a method of tracking multiple ground targets from an unmanned aerial vehicle (UAV) in a 3D reference frame. The tracking method uses a monocular camera and makes no assumptions on the shape of the terrain or the target motion. The UAV runs two cascaded estimators. The first is an Extended Kalman Filter (EKF), which is responsible for tracking the UAV’s state, such as position and velocity relative to a fixed frame. The second estimator is an EKF that is responsible for estimating a fixed number of landmarks within the camera’s field of view. Landmarks are parameterized by a quaternion associated with bearing from the camera’s optical axis and an inverse distance parameter. The bearing quaternion allows for a minimal representation of each landmark’s direction and distance, a filter with no singularities, and a fast update rate due to few trigonometric functions. Three methods for estimating the ground target positions are demonstrated: the first uses the landmark estimator directly on the targets, the second computes the target depth with a weighted average of converged landmark depths, and the third extends the target’s measured bearing vector to intersect a ground plane approximated from the landmark estimates. Simulation results show that the third target estimation method yields the most accurate results.
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- Award ID(s):
- 1650547
- PAR ID:
- 10053365
- Date Published:
- Journal Name:
- Proceedings of the International Technical Meeting of the Satellite Division of the Institute of Navigation (ION GNSS+)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
