Transferring synthetic lidar system data to real world domain for autonomous vehicle training applications

1. A method of correlating or transforming synthetic LiDAR data to a real-world domain for training an autonomous vehicle, the method comprising, by a processing device:

obtaining a data set of synthetic LiDAR data;

obtaining a plurality of images comprising representations of real-world objects;

transferring the synthetic LiDAR data to a two-dimensional representation by:

generating a plurality of two-dimensional front-view images by converting each 3D point of the synthetic LiDAR data into a pixel, the two-dimensional front-view images comprising representations of simulated objects;

obtaining a mapping that relates one or more of the simulated objects to one or more of the real-world objects represented in a plurality of images; and

annotating the plurality of two-dimensional front-view images by adding bounding boxes around the simulated objects in accordance with the mapping;

using the two-dimensional representation to train a model of the real-world environment; and

using the trained model of the real-world environment to train an autonomous vehicle.

2. The method of claim 1 , wherein using the two-dimensional representation to train the model of the real-world environment comprises inputting two-dimensional front view images generated from the synthetic LiDAR data into a convolutional neural network.

3. The method of claim 2 , further comprising generating the two-dimensional front view images from the synthetic LiDAR data by extracting front view depth images from the synthetic LiDAR data, in which substantially each pixel of each front view depth image is associated with a corresponding depth measurement.

4. The method of claim 1 , wherein obtaining the data set of synthetic LiDAR data comprises using a simulation application to generate the data set of synthetic LiDAR data by operating a virtual vehicle in a simulated environment of the simulation application.

5. The method of claim 4 , wherein using the simulation application to generate the data set of synthetic LiDAR data comprises, as the virtual vehicle is operated in the simulated environment:

accessing a plurality of depth images generated by the simulation;

extracting samples from a plurality of image frames generated by the simulation; and

correlating the extracted samples to the depth images to approximate each extracted sample to a pixel in one or more of the depth images.

6. The method of claim 1 , wherein transferring the synthetic LiDAR data to the two-dimensional representation comprises transferring the synthetic LiDAR data to a birds-eye view representation.

7. The method of claim 1 , further comprising generating ground truth annotations in a plurality of frames of synthetic LiDAR data to identify one or more objects in the synthetic LiDAR data.

8. The method of claim 1 , wherein:

the method further comprises generating ground truth annotations in a plurality of frames of the data set of synthetic LiDAR data to identify one or more objects in the synthetic LiDAR data; and

wherein at least some of the plurality of two-dimensional front-view images comprise one or more of the ground truth annotations that identify one or more objects.

9. The method of claim 1 wherein using the two-dimensional representation to train the model of the real-world environment comprises using the plurality of images comprising representations of the real-world objects, the two-dimensional front-view images from the LiDAR data, and a result of the mapping to train the model of the real-world environment.

10. A system for correlating synthetic LiDAR data to a real-world domain for an autonomous vehicle, the system comprising:

a processor;

a non-transitory memory that stores a data set of synthetic LiDAR data; and

a non-transitory memory containing programming instructions that are configured to cause the processor to:

cause the system to receive a plurality of images captured from a real-world environment;

access the data set of synthetic LiDAR data and extract synthetic LiDAR data from the data set,

transfer the extracted synthetic LiDAR data to a two-dimensional representation by:

generating a plurality of two-dimensional front-view images by converting each 3D point of the synthetic LiDAR data into a pixel, the two-dimensional front-view images comprising representations of simulated objects; and

obtaining a mapping that relates one or more of the simulated objects to one or more of the real-world objects represented in the plurality of images; and

annotating the plurality of two-dimensional front-view images by adding bounding boxes around the simulated objects in accordance with the mapping;

use the two-dimensional representation to train a model of the real-world environment; and

use the trained model of the real-world environment to train an autonomous vehicle.

11. The system of claim 10 , wherein the instructions to use the two-dimensional representation to train the model of the real-world environment comprise instructions to input two-dimensional front view images generated from the synthetic LiDAR data into a convolutional neural network.

12. The system of claim 11 , further comprising instructions to generate the two-dimensional front view images from the synthetic LiDAR data by extracting front view depth images from the synthetic LiDAR data, in which substantially each pixel of each front view depth image is associated with a corresponding depth measurement.

13. The system of claim 10 , further comprising additional programming instructions that are configured to cause a processor to implement a simulation application that generates the data set of synthetic LiDAR data by operating a virtual vehicle in a simulated environment of the simulation application.

14. The system of claim 13 , wherein the programming instructions to implement the simulation application that generates the data set of synthetic LiDAR data comprise instructions to, as the virtual vehicle is operated in the simulated environment:

access a plurality of depth images generated by the simulation;

extract samples from a plurality of image frames generated by the simulation; and

correlate the extracted samples to the depth images to approximate each extracted sample to a pixel in one or more of the depth images.

15. The system of claim 10 , wherein the programming instructions to transfer the synthetic LiDAR data to the two-dimensional representation comprises instructions to transfer the synthetic LiDAR data to a birds-eye view representation.

16. The system of claim 10 , further comprising additional programming instructions that are configured to cause the processor to generate ground truth annotations in a plurality of frames of synthetic LiDAR data to identify one or more objects in the synthetic LiDAR data.

17. The system of claim 10 :

further comprising additional programming instructions that are configured to cause the processor to generate ground truth annotations in a plurality of frames of the data set of synthetic LiDAR data to identify one or more objects in the synthetic LiDAR data; and

wherein at least some of the plurality of two-dimensional front-view images comprise one or more of the ground truth annotations that identify one or more objects.

18. The system of claim 10 , wherein the instructions to use the two-dimensional representation to train the model of the real-world environment comprise instructions to use the plurality of images captured from the real-world environment, the two-dimensional front-view images from the LiDAR data, and a result of the mapping to train the model of the real-world environment.

19. A non-transitory computer-readable medium containing programming instructions for correlating synthetic LiDAR data to a real-world domain and training an autonomous vehicle, the instructions comprising instructions to:

receive a plurality of images captured from a real-world environment;

implement a simulation application that generates a data set of synthetic LiDAR data by operating a virtual vehicle in a simulated environment of the simulation application;

extract synthetic LiDAR data from the data set;

transfer the extracted synthetic LiDAR data to a two-dimensional representation by:

generating a plurality of two-dimensional front-view images by converting each 3D point of the synthetic LiDAR data into a pixel, the two-dimensional front-view images comprising representations of simulated objects;

obtaining a mapping that relates one or more of the simulated objects to one or more of the real-world objects represented in the plurality of images; and

annotating the plurality of two-dimensional front-view images by adding bounding boxes around the simulated objects in accordance with the mapping; and

use the two-dimensional representation to train a model of the real-world environment.