Methods and apparatus to simulate sensor data

1. An apparatus for simulating sensor data, the apparatus comprising:

at least one memory;

machine readable instructions; and

a processor to execute the machine readable instructions to:

extract a noise characteristic associated with a feature present in first sensor data obtained by a physical sensor;

identify a feature present in second sensor data, the second sensor data generated by an environment simulator simulating a virtual representation of the physical sensor; and

synthesize noise-adjusted simulated sensor data based on a combination of:

a first loss function based on noise characteristics of the noise-adjusted simulated sensor data and the feature present in the first sensor data; and

a second loss function based on the noise-adjusted simulated sensor data and the feature identified in the second sensor data.

2. The apparatus of claim 1 , wherein the processor is to access the second sensor data generated by the environment simulator.

3. The apparatus of claim 1 , wherein the processor is to store the extracted noise characteristic to and synthesize the noise-adjusted simulated sensor data based on the noise characteristic stored.

4. The apparatus of claim 1 , wherein the second sensor data represents a virtualized version of a same type of sensor represented by the first sensor data.

5. The apparatus of claim 1 , wherein the processor is to identify of a feature present in second sensor data using a convolutional neural network.

6. The apparatus of claim 5 , wherein the convolutional neural network is a Visual Geometry Group convolutional neural network.

7. The apparatus of claim 1 , wherein the processor is to perform a stochastic gradient descent to select a weighting value applied to the first loss function.

8. At least one non-transitory machine-readable storage medium comprising instructions which, when executed, cause a processor to at least:

extract a noise characteristic associated with a feature present in first sensor data, the first sensor data captured by a physical sensor;

identify a feature present in second sensor data, the second sensor data generated by an environment simulator simulating a virtual representation of the physical sensor; and

synthesize noise-adjusted simulated sensor data based on a combination of:

a first loss function based on noise characteristics of the noise-adjusted simulated sensor data and the feature present in first sensor data; and

a second loss function based on the noise-adjusted simulated sensor data and the feature identified in the second sensor data.

9. The at least one non-transitory machine-readable medium of claim 8 , wherein the instructions, when executed, further cause the processor to store the extracted noise characteristic in a sensor noise characteristic data store, wherein the synthesizing of the noise-adjusted simulated sensor data is further based on the noise characteristic stored in the sensor noise characteristic data store.

10. The at least one non-transitory machine-readable medium of claim 8 , wherein the instructions, when executed, further cause the processor to identify the feature present in the second sensor data using a convolutional neural network.

11. The at least one non-transitory machine-readable medium of claim 10 , wherein the convolutional neural network is a Visual Geometry Group convolutional neural network.

12. The at least one non-transitory machine-readable medium of claim 8 , wherein the instructions, when executed, cause the processor to perform a stochastic gradient descent to select a weighting value applied to the first loss function.

13. A method for simulating sensor data, the method comprising:

extracting, by executing an instruction with a processor, a noise characteristic associated with a feature present in first sensor data, the first sensor data captured by a physical sensor;

identifying, by executing an instruction with the processor, a feature present in second sensor data, the second sensor data generated by an environment simulator simulating a virtual representation of the physical sensor; and

synthesizing, by executing an instruction with the processor, noise-adjusted simulated sensor data based on a combination of:

a first loss function representing noise characteristics of the noise-adjusted simulated sensor data and the feature present in the first sensor data; and

a second loss function based on the noise-adjusted simulated sensor data and the features identifies in the second sensor data.

14. The method of claim 13 , further including storing the extracted noise characteristic in a sensor noise characteristic data store, and wherein the synthesizing of the noise-adjusted simulated sensor data is further based on the noise characteristic stored in the sensor noise characteristic data store.

15. The method of claim 13 , wherein the identifying of the feature present in the simulated sensor data is performed using a convolutional neural network.

16. The method of claim 13 , wherein the convolutional neural network is a Visual Geometry Group convolutional neural network.

17. The method of claim 13 , wherein the synthesizing further includes performing a stochastic gradient descent to select a weighting value applied to the first loss function.

18. An apparatus for simulating sensor data, the apparatus comprising:

means for extracting a noise characteristic associated with a feature present in first sensor data, the first sensor data captured by a physical sensor;

means for identifying a feature present in second sensor data, the second sensor data generated by an environment simulator simulating a virtual representation of the physical sensor; and

means for synthesizing noise-adjusted simulated sensor data based on a combination of:

a first loss function based on noise characteristics associated with of the noise-adjusted simulated sensor data and the feature present in the first sensor data; and

a second loss function based on the noise-adjusted simulated sensor data and the feature identified in the second sensor data.

19. The apparatus of claim 18 , wherein the means for extracting is to store the extracted noise characteristic in a sensor noise characteristic data store, and wherein the means for synthesizing is further to synthesize the noise-adjusted simulated sensor data based on the noise characteristic stored in the sensor noise characteristic data store.

20. The apparatus of claim 18 , means for identifying is implemented using a convolutional neural network.

21. The apparatus of claim 18 , wherein the means for synthesizing is to perform a stochastic gradient descent to select a weighting value applied to the first loss function.