Stochastic map-aware stereo vision sensor model

1. A method of defining a sensor model, comprising:

determining a probability of obtaining a measurement from a plurality of potential causes in a field of view of a sensor calculating a grid-based stochastic map of an environment, the grid-based stochastic map comprising a mean occupancy level for each voxel in the grid-based stochastic map and a variance of the mean occupancy level for each voxel, an occupancy level indicating whether a voxel is full, partially full, or empty, and each voxel corresponding to a cell in a grid of the grid-based stochastic map;

determining a probability of obtaining an image of an intended target of a robot at a current location of the robot based on the determined probability of obtaining the measurement;

updating the mean occupancy level and the variance for voxels in the field of view based on the probability of obtaining the measurement;

planning an action for the robot based on the probability of obtaining the image; and

navigating, the robot, through the environment based on the planned action.

2. The method of claim 1 , in which determining the probability of obtaining the measurement further comprises, for each potential cause of the plurality of potential causes:

calculating a likelihood of the measurement given the cause;

determining a probability of the potential cause in the grid-based stochastic map; and

combining measurement likelihoods for the plurality of potential causes and a probability of the plurality of potential causes:

to determine the probability of obtaining the measurement, and

to define the sensor model.

3. The method of claim 1 , further comprising inferring a presence of the intended target in the environment based on the determined probability of obtaining the image.

4. The method of claim 1 , in which the field of view is for one pixel of a plurality of pixels from an entire field of view of the sensor.

5. The method of claim 1 , in which the planned action comprises:

a path to the intended target when the probability of obtaining the image is greater than a threshold; and

additional measurements at a new location when the probability of obtaining the image is less than the threshold.

6. An apparatus for defining a sensor model, the apparatus comprising:

means for determining a probability of obtaining a measurement from a plurality of potential causes in a field of view of a sensor calculating a grid-based stochastic map of an environment, the grid-based stochastic map comprising a mean occupancy level for each voxel in the grid-based stochastic map and a variance of the mean occupancy level for each voxel, an occupancy level indicating whether a voxel is full, partially full, or empty, and each voxel corresponding to a cell in a grid of the grid-based stochastic map;

means for determining a probability of obtaining an image of an intended target of a robot at a current location of the robot based on the determined probability of obtaining the measurement;

means for updating the mean occupancy level and the variance for voxels in the field of view based on the probability of obtaining the measurement;

means for planning an action for the robot based on the probability of obtaining the image; and

means for navigating, the robot, through the environment based on the planned action.

7. The apparatus of claim 6 , in which the sensor comprises a stereoscopic camera.

8. The apparatus of claim 6 , in which the means for determining the probability of obtaining the measurement further comprises, for each potential cause of the plurality of potential causes:

means for calculating a likelihood of the measurement given the cause;

means for determining a probability of the potential cause in the grid-based stochastic map; and

means for combining measurement likelihoods for the plurality of potential causes and a probability of the plurality of potential causes:

to determine the probability of obtaining the measurement, and

to define the sensor model.

9. The apparatus of claim 6 , further comprising means for inferring a presence of the intended target in the environment based on the determined probability of obtaining the image.

10. The apparatus of claim 6 , in which the field of view is for one pixel of a plurality of pixels from an entire field of view of the sensor.

11. An apparatus for defining a sensor model, the apparatus comprising:

a memory; and

at least one processor coupled to the memory, the at least one processor configured:

to determine a probability of obtaining a measurement from a plurality of potential causes in a field of view of a sensor calculating a grid-based stochastic map of an environment, the grid-based stochastic map comprising a mean occupancy level for each voxel in the grid-based stochastic map and a variance of the mean occupancy level for each voxel, an occupancy level indicating whether a voxel is full, partially full, or empty, and each voxel corresponding to a cell in a grid of the grid-based stochastic map;

to determine a probability of obtaining an image of an intended target of a robot at a current location of the robot based on the determined probability of obtaining the measurement;

to update the mean occupancy level and the variance for voxels in the field of view based on the probability of obtaining the measurement;

to plan an action for the robot based on the probability of obtaining the image; and

to navigate, the robot, through the environment based on the planned action.

12. The apparatus of claim 11 , in which the sensor comprises a stereoscopic camera.

13. The apparatus of claim 11 , in which the at least one processor is further configured, for each potential cause of the plurality of potential causes:

to calculate a likelihood of the measurement given the cause;

to determine a probability of the potential cause in the grid-based stochastic map; and

to combine measurement likelihoods for the plurality of potential causes and a probability of the plurality of potential causes:

to determine the probability of obtaining the measurement, and

to define the sensor model.

14. The apparatus of claim 11 , in which the at least one processor is further configured to infer a presence of the intended target in the environment based on the determined probability of obtaining the image.

15. The apparatus of claim 11 , in which the field of view is for one pixel of a plurality of pixels from an entire field of view of the sensor.

16. A non-transitory computer-readable medium having program code recorded thereon for defining a sensor model, the program code executed by a processor and comprising:

program code to determine a probability of obtaining a measurement from a plurality of potential causes in a field of view of a sensor calculating a grid-based stochastic map of an environment, the grid-based stochastic map comprising a mean occupancy level for each voxel in the grid-based stochastic map and a variance of the mean occupancy level for each voxel, an occupancy level indicating whether a voxel is full, partially full, or empty, and each voxel corresponding to a cell in a grid of the grid-based stochastic map;

program code to determine a probability of obtaining an image of an intended target of a robot at a current location of the robot based on the determined probability of obtaining the measurement;

program code to update the mean occupancy level and the variance for voxels in the field of view based on the probability of obtaining the measurement;

program code to plan an action for the robot based on the probability of obtaining the image; and

program code to navigate, the robot, through the environment based on the planned action.

17. The non-transitory computer-readable medium of claim 16 , in which the sensor comprises a stereoscopic camera.

18. The non-transitory computer-readable medium of claim 16 , in which the program code further comprises, for each potential cause of the plurality of potential causes:

program code to calculate a likelihood of the measurement given the cause;

program code to determine a probability of the potential cause in the grid-based stochastic map; and

program code to combine measurement likelihoods for the plurality of potential causes and a probability of the plurality of potential causes:

to determine the probability of obtaining the measurement, and

to define the sensor model.

19. The non-transitory computer-readable medium of claim 16 , in which the program code further comprises program code to infer a presence of the intended target in the environment based on the determined probability of obtaining the image.

20. The non-transitory computer-readable medium of claim 16 , in which the field of view is for one pixel of a plurality of pixels from an entire field of view of the sensor.