US 11,140,379 B2 · Mapping and tracking system with features in three-dimensional space

Abstract

LK-SURF, Robust Kalman Filter, HAR-SLAM, and Landmark Promotion SLAM methods are disclosed. LK-SURF is an image processing technique that combines Lucas-Kanade feature tracking with Speeded-Up Robust Features to perform spatial and temporal tracking using stereo images to produce 3D features can be tracked and identified. The Robust Kalman Filter is an extension of the Kalman Filter algorithm that improves the ability to remove erroneous observations using Principal Component Analysis and the X84 outlier rejection rule. Hierarchical Active Ripple SLAM is a new SLAM architecture that breaks the traditional state space of SLAM into a chain of smaller state spaces, allowing multiple tracked objects, multiple sensors, and multiple updates to occur in linear time with linear storage with respect to the number of tracked objects, landmarks, and estimated object locations. In Landmark Promotion SLAM, only reliable mapped landmarks are promoted through various layers of SLAM to generate larger maps.

Claims16

Dependent claims

A method for managing objects in a multi-dimensional space when the objects are no longer visible in a collection of images captured from the multi-dimensional space over time, the method comprising:

identifying one or more objects visible in a first image among the collection of images;
adding the identified objects to a first list of tracked identified objects, wherein the first list represents a state space of the multi-dimensional space, the state space comprising object identifiers, a state vector, and a covariance matrix;
tracking the identified objects from the first list of objects in one or more subsequent images among the collection of images, the one or more subsequent images being captured after capturing the first image;
determining whether the tracked identified objects are absent in the one or more subsequent images, wherein determining includes determining whether a tracked identified object from the first list is an unreliable object and includes mapping a high-dimensional feature into a one-dimensional space, wherein the high-dimensional feature is associated with one of the tracked identified objects captured from a high-dimensional observation;
and
removing, based on the determination, the tracked identified objects from the first list, wherein removing includes applying an outlier rejection rule in the one-dimensional space to remove the mapped high-dimensional feature.

3 more elements

A system managing objects in a multi-dimensional space when the objects are no longer visible in a collection of images captured from the multi-dimensional space over time, the system comprising:

a processor;
and
a memory communicatively coupled to the processor when the system is operational, the memory bearing processor instructions that, when executed on the processor, cause the system to at least:
identify one or more objects visible in a first image among the collection of images;
add the identified objects to a first list of tracked identified objects, wherein the first list represents a state space of the multi-dimensional space, the state space comprising object identifiers, a state vector, and a covariance matrix;
track the identified objects from the first list of objects in one or more subsequent images among the collection of images, the one or more subsequent images being captured after capturing the first image;
determine whether the tracked identified objects are absent in the one or more subsequent images, wherein causing the system to determine includes causing the system to determine whether a tracked identified object from the first list is an unreliable object and includes causing the system to map a high-dimensional feature into a one-dimensional space, wherein the high-dimensional feature is associated with one of the tracked identified objects captured from a high-dimensional observation;
and
remove, based on the determination, the tracked identified objects from the first list, wherein causing the system to remove includes causing the system to apply an outlier rejection rule in the one-dimensional space to remove the mapped high-dimensional feature.

6 more elements

Dependencies are inferred from the claim text.

Mapping and tracking system with features in three-dimensional space

Life of this patent

Mapping and tracking system with features in three-dimensional space

Life of this patent

Abstract

Claims16