Reality capture robot for generating a map of an environment

1 . A method comprising:

determining, by one or more computer processors coupled to memory, at least a portion of an environment to be scanned by a robotic device comprising one or more three dimensional (3D) imaging sensors;

causing the robotic device to autonomously scan the at least the portion of the environment using the one or more 3D imaging sensors;

receiving, from the robotic device, sensor data generated by the one or more 3D imaging sensors;

generating, based at least in part on the sensor data, a 3D spatial map representing the at least the portion of the environment;

identifying, based at least in part on layout data, a layout of the environment, the layout including:

a first location of a first object within the environment, and

a first representation associated with the first object;

determining at least one discrepancy between the 3D spatial map and the layout, the at least one discrepancy including a second location of a second object within the environment, the second object being the same as the first object;

determining, based at least in part on the first representation, a second representation associated with the second object within the 3D spatial map;

determining a third object and a fourth object within the 3D spatial map;

determining that the third object represents a static object within the environment;

determining that the fourth object represents a dynamic object within the environment;

generating, based at least in part on the at least one discrepancy, a map of the environment, the map including the second representation associated with the second object and the third object, wherein the map omits the fourth object;

determining, based at least in part on the map, a route for the second robotic device to complete a task within the environment; and

sending route data associated with the route to the second robotic device, wherein the second robotic device uses the route data to travel along the route.

2 . The method of claim 1 , further comprising sending map data associated with the map to a second robotic device, wherein the second robotic device is configured to utilize the map data to traverse the environment.

3 . The method of claim 1 , wherein:

the 3D spatial map represents a first 3D map of the environment;

the layout represents a second 3D map of the environment; and

the map represents a third 3D map of the environment.

4 . The method of claim 1 , wherein the sensor data is based at least in part on:

first sensor data generated by the one or more 3D imaging sensors at a third location of the robotic device within the at least the portion of the environment; and

second sensor data generated by the one or more 3D imaging sensors at a fourth location of the robotic device within the at least the portion of the environment.

5 . The method of claim 1 , wherein the static object is one of a frame, structure, or pillar.

6 . The method of claim 1 , further comprising:

determining an amount of time that has elapsed since generating the map;

determining that the amount of time is greater than a threshold; and

based at least in part on the amount of time being greater than the threshold, causing the robotic device or a second robotic device to scan at least a second portion of the environment.

7 . A method comprising:

determining at least a portion of an environment to be scanned by a first robotic device; receiving, from the first robotic device, sensor data generated by one or more sensors of the first robotic device;

generating, based at least in part on the sensor data, a three dimensional (3D) spatial map representing the at least the portion of the environment;

identifying, based at least in part on layout data, a layout of the environment, the layout being representative of one or more objects within the environment;

aligning the 3D spatial map and at least a portion of the layout;

determining, based at least in part on aligning the 3D spatial map and the at least the portion of the layout, a discrepancy between a first location of a first instance of an object in the 3D spatial map and a second location of a second instance of the object in the layout;

determining a second object and a third object within the 3D spatial map, wherein the second object represents a static object within the environment, and the third object represents a dynamic object within the environment;

generating, based at least in part on the discrepancy, a map of the environment including the object and the second object, and wherein the map omits the third object;

determining a first classifier associated with the object in the layout;

determining, based at least in part on the first classifier, a second classifier of the object;

sending, to a second robotic device, data associated with the map, the second robotic device being configured to utilize the data to traverse the environment;

determining, based at least in part on the map, a route for the second robotic device to complete a task within the environment; and

sending route data associated with the route to the second robotic device, wherein the second robotic device uses the route data to travel along the route.

8 . The method of claim 7 , further comprising:

determining a route along which the first robotic device is to travel to scan the at least the portion of the environment; and

sending route data associated with the route to the first robotic device, wherein the first robotic device uses the route data to autonomously travel along the route.

9 . The method of claim 8 , wherein:

the sensor data is received as the first robotic device travels along the route; or

the sensor data is received upon the first robotic device completing the route.

10 . The method of claim 7 , wherein the map includes the second classifier of the object.

11 . The method of claim 7 , further comprising:

providing, as an input to a machine-learned model, the sensor data;

receiving, as an output of the machine-learned model, an indication of a first classifier of the first instance of the object; and

determining, based at least in part on the layout data, a second classifier of the second instance of the object,

wherein aligning the 3D spatial map with the at least the portion of the layout is based at least in part on the first classifier and the second classifier.

12 . The method of claim 7 , further comprising:

determining at least one of:

a first origin associated with the 3D spatial map, or

a first reference point within the 3D spatial map; and

determining at least one of:

a second origin associated with the at least the portion of the layout, or

a second reference point within the at least the portion of the layout,

wherein aligning the 3D spatial map and the at least the portion of the layout is based at least in part on at least one of:

aligning the first origin with the second origin, or

aligning the first reference point with the second reference point.

13 . The method of claim 7 , wherein the first instance of the object represents a first pillar extending from a floor of the environment, further comprising:

determining a second object within the 3D spatial map, the second object representing a second pillar extending from the floor of the environment; and

determining, based at least in part on the map of the environment, a distance extending between the first pillar and the second pillar.

14 . The method of claim 7 , wherein determining the second classifier of the object is based at least in part aligning the 3D spatial map and the at least the portion of the layout.

15 . The method of claim 7 , wherein determining the discrepancy is based at least in part on first coordinates associated with the first location of the first instance of the object in the 3D spatial map and second coordinates associated with the second location of the second instance of the object in the layout.