公开(公告)号：US20230243658A1

公开(公告)日：2023-08-03

申请号：US18004614

申请日：2021-07-06

Applicant: Ohio State Innovation Foundation

Inventor： Alper Yilmaz ,  Bing Zha

IPC: G01C21/30 ,  G01C21/16 ,  G06N3/0442 ,  G06N3/063

CPC classification number: G01C21/30 ,  G01C21/1656 ,  G06N3/0442 ,  G06N3/063

Abstract: An object of initial unknown position on a map may be determined by traversing through moving and turning to establish motion trajectory to reduce its spatial uncertainty to a single location that would fit only to a certain map trajectory. A artificial neural network model learns from object motion on different map topologies may establish the object's end-to-end positioning from embedding map topologies and object motion. The proposed method includes learning potential motion patterns from the map and perform trajectory classification in the map's edge-space. Two different trajectory representations, namely angle representation and augmented angle representation (incorporates distance traversed) are considered and both a Graph Neural Network and an RNN are trained from the map for each representation to compare their performances. The results from the actual visual-inertial odometry have shown that the proposed approach is able to learn the map and localize the object based on its motion trajectories.