The Augmented Gaussian Sum Filter unifies Gaussian sum filters and particle filters via an augmented Gaussian approximation with tunable covariances that interpolates continuously between the two behaviors.
A new approach to linear filtering and prediction problems
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
representative citing papers
EnDKF combines ensemble Kalman filtering with directional statistics and unit quaternions to achieve lower pose tracking error than raw measurements in synthetic constant-velocity tests and FoundationPose-based head tracking.
MfNeuPAN uses multi-frame observations and predicted future obstacle paths to enable proactive end-to-end robot navigation that improves robustness in unknown dynamic environments.
citing papers explorer
-
A Gaussian Sum Filter for Unifying Gaussian and Particle Filters
The Augmented Gaussian Sum Filter unifies Gaussian sum filters and particle filters via an augmented Gaussian approximation with tunable covariances that interpolates continuously between the two behaviors.
-
Pose Tracking with a Foundation Pose Model and an Ensemble Directional Kalman Filter
EnDKF combines ensemble Kalman filtering with directional statistics and unit quaternions to achieve lower pose tracking error than raw measurements in synthetic constant-velocity tests and FoundationPose-based head tracking.
-
MfNeuPAN: Proactive End-to-End Navigation in Dynamic Environments via Direct Multi-Frame Point Constraints
MfNeuPAN uses multi-frame observations and predicted future obstacle paths to enable proactive end-to-end robot navigation that improves robustness in unknown dynamic environments.
- DPNet: Doppler LiDAR Motion Planning for Highly-Dynamic Environments