US 11,915,487 B2
System and method for self-supervised depth and ego-motion overfitting
Rares A. Ambrus, San Francisco, CA (US); Vitor Guizilini, Santa Clara, CA (US); Sudeep Pillai, Santa Clara, CA (US); and Adrien David Gaidon, San Jose, CA (US)
Assigned to TOYOTA RESEARCH INSTITUTE, INC., Los Altos, CA (US)
Filed by TOYOTA RESEARCH INSTITUTE, INC., Los Altos, CA (US)
Filed on May 5, 2020, as Appl. No. 16/867,124.
Prior Publication US 2021/0350222 A1, Nov. 11, 2021
Int. Cl. G06V 20/56 (2022.01); G06N 3/08 (2023.01); G06T 7/50 (2017.01); G06F 18/214 (2023.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01)
CPC G06V 20/56 (2022.01) [G06F 18/214 (2023.01); G06N 3/08 (2013.01); G06T 7/50 (2017.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01)] 17 Claims
OG exemplary drawing
 
1. A system for generating depth estimates of an environment, comprising:
one or more processors;
a memory communicably coupled to the one or more processors and storing:
a depth system including instructions that when executed by the one or more processors cause the one or more processors to generate a plurality of depth maps by:
receiving a plurality of monocular images, each of the plurality of monocular images capturing substantially the same environment of interest;
processing each of the monocular images according to an overfit depth model;
filtering the plurality of depth maps to remove non-static objects by comparing non-consecutive depth maps of a route trajectory;
an image module including instructions that when executed by the one or more processors cause the one or more processors to generate a permanent three-dimensional reconstruction of the environment and the route trajectory including locations of obstacles based on the plurality of depth maps; and
a planner module to plan a trajectory of a car that safely navigates the obstacles.