Patent ID: 11935284
Assignee: nan
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 0:
1. A method of classifying objects for robotic situational awareness, based on robotic observations obtained from multiple sequential viewpoints, the method implemented by at least one processor according to instructions stored in non-transient memory storage, the method comprising:
from a training set of sequential observations and corresponding viewpoints, generating, for each class of a set of classes of objects, a viewpoint-dependent class model representing each class as a Gaussian process, wherein each class model includes spatial correlations between the viewpoints in classifier measurements for the corresponding observations;
subsequently:
A. acquiring a history Hk of multiple sequential robotic observations and controls up to a time k, including an observation at time k, denoted as zk;
B. applying a classifier to generate from zk a set Sk of classifier measurements, wherein the distribution of Sk represents a model uncertainty of the classifier, wherein each classifier measurement sk of the set Sk is a vector, each component of which indicates a likelihood of an object belonging to a class represented by the component;
C. sampling a trajectory X0:k and a pose o of an object from a joint pose distribution of trajectories and object poses, given the history Hk;
D. sampling a classifier measurement sk from the set Sk;
E. for each component s(i) of the sampled classifier measurement sk, applying the class model for a class c represented by the component to calculate a likelihood of s(i) belonging to the class c, given observations prior to time k;
F. from the likelihood of each s(i), generating a likelihood value P(sk|c,Hk\{zk}), representing a likelihood value of sk belonging to a given class c, wherein the likelihood value of sk is calculated by a function employing the spatial correlations of the class model of the given class c and a model uncertainty in past classifier measurements;
G. generating, from the likelihood value of sk, a normalized class likelihood value P(c|sk,Hk);
H. generating an average class likelihood, P(c|Hk(i)), for the given class c, given Hk with respect to the sampled trajectory, by repeating the calculation of the normalized class likelihood value for multiple samples sk, given the sampled trajectory;
I. generating, for multiple sampled trajectories of X0:k, multiple respective average class likelihoods;
J. averaging the average class likelihoods to generate a total average class likelihood P(c|Hk) for the given class c, given Hk, indicative of a probability of the object belonging to the given class c, given trajectory uncertainty, viewpoint-dependent variations, and model uncertainty of the classifier; and,
K. repeating steps G-J for each class of the set of classes of objects to determine the highest total average class likelihood P(c|Hk), and responsively generating the class and the total average class likelihood of the most likely class of the object.