Condition dependent parameters for large-scale localization and/or mapping

1. A system comprising:

one or more processors; and

one or more computer-readable media storing instructions executable by the one or more processors, wherein the instructions, when executed, cause the system to perform operations comprising:

receiving LIDAR data from a LIDAR sensor of an autonomous vehicle;

determining a location of the autonomous vehicle within a map based at least in part on the LIDAR data;

selecting, based at least in part on the location, a parameter associated with a configuration of a mapping component that creates or updates the map based at least in part on the LIDAR data; and

updating the map, based at least in part on additional LIDAR data and the parameter, of an environment through which the autonomous vehicle traverses.

2. The system of claim 1 , wherein:

the parameter is determined based at least in part on minimizing a score associated with the map;

and

selecting the parameter comprises modifying at least one of the LIDAR data or the mapping component.

3. The system of claim 1 , wherein the operations further comprise modifying a sensor parameter based at least in part on the location, wherein modifying the sensor parameter comprises adjusting at least one of:

a sensor data accumulation period;

an uncertainty measurement associated with the sensor data;

an instruction to discard at least a portion of the sensor data for map generation;

a search radius associated with a search for sensor data that corresponds to a same portion of the environment;

a firing rate of the LIDAR sensor;

a spin speed of the LIDAR sensor; or

a frequency of attempted alignments of sensor data from different sensors to make within a distance traversed by the autonomous vehicle.

4. The system of claim 1 , wherein selecting the parameter comprises at least one of:

determining, using a lookup table, one or more values associated with the location;

determining, based on one or more of an interpolation or extrapolation, the parameter; or

modifying, based at least in part on a score associated with the map, a value of the parameter.

5. The system of claim 1 , wherein the instructions further cause the system to control, based at least in part on the map and the location, the autonomous vehicle.

6. The system of claim 1 , wherein the instructions further cause the system to:

determine, based at least in part on the LIDAR data, a classification of the environment, and

wherein selecting the parameter is further based at least in part on the classification.

7. The system of claim 6 , wherein the classification comprises at least one of:

a city;

a tunnel;

a bridge;

a rural area; or

a highway.

8. A method comprising:

receiving first sensor data from a sensor of a vehicle;

determining, based at least in part on the first sensor data, a condition associated with at least one of the vehicle or an environment surrounding the vehicle;

determining, based at least in part on the condition, a parameter associated with a configuration of a mapping algorithm;

adjusting, based at least in part on the parameter, operation of the mapping algorithm; and

updating the map, based at least in part on additional LIDAR data and the parameter.

9. The method of claim 8 , further comprising:

receiving second sensor data from the sensor;

determining, based at least in part on the second sensor data and the adjustment, a map; and

controlling the vehicle based at least in part on the map.

10. The method of claim 8 , wherein determining the parameter comprises:

receiving, from the sensor, third sensor data;

determining, from the third sensor data, the condition;

generating, based at least in part on the third sensor data, a map;

determining, based at least in part on the map, a score;

minimizing the score, based at least in part on modifying the third sensor data, operation of the mapping algorithm, or operation of the sensor; and

determining, as the parameter, a state achieved by the modification.

11. The method of claim 8 , wherein the condition comprises at least one of a vehicle speed, a vehicle location, a vehicle orientation, semantic information associated with sensor data, a vehicle sensor quality, an indication of a density of features of an environment surrounding the vehicle, or an indication of weather of the environment.

12. The method of claim 8 , further comprising:

determining, based at least in part on the first sensor data, a classification of the environment; and

associating the parameter with the classification.

13. The method of claim 8 ,

wherein the method further comprises determining, based at least in part on the sensor data, a location of the autonomous vehicle, and

wherein the condition comprises the location.

14. The method of claim 8 , wherein the method further comprises adjusting operation of the sensor based at least in part on the parameter and wherein the parameter comprises at least one of:

a sensor data accumulation period;

an uncertainty measurement associated with the sensor data;

an instruction to discard at least a portion of the sensor data for map generation;

a search radius associated with a search for sensor data that corresponds to a same portion of the environment;

a firing rate of the sensor;

an intrinsic or extrinsic setting of the sensor; or

a frequency of attempted alignments of sensor data from different sensors to make within a distance traversed by the autonomous vehicle.

15. A non-transitory computer-readable medium having a set of instructions that, when executed, cause one or more processors to perform operations comprising:

receiving sensor data from a sensor of an autonomous vehicle;

determining, based at least in part on the sensor data, a condition associated with at least one of the autonomous vehicle or an environment surrounding the autonomous vehicle;

adjusting, as an adjusted parameter and based at least in part on the condition, a parameter of a mapping component to increase clarity of a map generated by the mapping component;

determining, based at least in part on the sensor data and the adjusted parameter, a map of the environment and a position of the autonomous vehicle in the environment; and

updating the map, based at least in part on additional LIDAR data and the adjusted parameter.

16. The non-transitory computer-readable medium of claim 15 , wherein the condition comprises at least one of a characteristic of the sensor data or a classification associated with the environment.

17. The non-transitory computer-readable medium of claim 15 , wherein the operations further comprise controlling, based at least in part on at least one of the map or the position, operation of the autonomous vehicle.

18. The non-transitory computer-readable medium of claim 15 , wherein determining the map and the position comprises at least one of:

altering, based at least in part on the adjusted parameter, the sensor data to determine altered sensor data; or

modifying, based at least in part on the adjusted parameter, operation of the sensor to change output of the sensor.

19. The non-transitory computer-readable medium of claim 15 , wherein the parameter comprises at least one of:

a sensor data accumulation period;

an uncertainty measurement associated with the sensor data;

an instruction to discard at least a portion of the sensor data for map generation;

a search radius associated with a search for sensor data that corresponds to a same portion of the environment;

a firing rate of the sensor;

an intrinsic or extrinsic setting of the sensor; or

a frequency of attempted alignments of sensor data from different sensors to make within a distance traversed by the autonomous vehicle.

20. The non-transitory computer-readable medium of claim 15 , wherein the map is a second map and the position is a second position and the operations further comprise:

determining, based at least in part on the sensor data and a default set of parameters, a first map and a first position;

determining a score associated with at least one of the first map or the first position; and

adjusting the default set of parameters to decrease the score associated with the first map.