Detection method for autonomous mobile device, autonomous mobile device and storage medium

Abstract

The present disclosure provides a detection method for an autonomous mobile device, an autonomous mobile device, and a storage medium. The detection method for the autonomous mobile device includes: obtaining an environmental map of an environment in which the autonomous mobile device is located, the environmental map being a grid map; obtaining grids-to-be-processed in the environmental map; clustering the grids-to-be-processed to obtain one or more groups of grids-to-be-processed; for each group of grids-to-be-processed, determining whether the group of grids-to-be-processed corresponds to a doorsill based on environmental information.

What is claimed is: 
     
         1 . A detection method for an autonomous mobile device, comprising:
 obtaining an environmental map of an environment in which the autonomous mobile device is located, the environmental map being a grid map;   obtaining grids-to-be-processed in the environmental map;   clustering the grids-to-be-processed to obtain one or more groups of grids-to-be-processed; and   for each group of grids-to-be-processed, when a number of grids-to-be-processed included in the group of grids-to-be-processed is within a predetermined range, determining whether the group of grids-to-be-processed corresponds to a doorsill.   
     
     
         2 . The detection method of  claim 1 , wherein determining whether the group of grids-to-be-processed corresponds to a doorsill comprises: determining whether the group of grids-to-be-processed corresponds to a doorsill based on environmental information. 
     
     
         3 . The detection method of  claim 2 , wherein the environmental information includes at least one of:
 obstacle information in a light detection and ranging map of the environment;   at least portion of video information of the environment; or   a structured light scanning result of the environment.   
     
     
         4 . The detection method of  claim 1 , wherein obtaining grids-to-be-processed in the environmental map comprises:
 for each grid of a plurality of grids traversed by the autonomous mobile device when moving in the environment:   detecting the grid through a floor sensor on the autonomous mobile device to obtain a floor sensor signal corresponding to the grid; and   determining that the grid is a grid-to-be-processed based on the floor sensor signal.   
     
     
         5 . The detection method of  claim 4 , wherein the floor sensor is an ultrasound sensor, the floor sensor signal is a reflected ultrasound wave,
 wherein detecting the grid through a floor sensor on the autonomous mobile device to obtain a floor sensor signal corresponding to the grid comprises:   transmitting, through the ultrasound sensor, a plurality of ultrasound pulses toward the grid and receiving a plurality of reflected ultrasound wave; and   wherein determining that the grid is a grid-to-be-processed based on the floor sensor signal comprises:   counting, in the plurality of reflected ultrasound waves, a number of reflected ultrasound waves having a reflected wave intensity within a predetermined ultrasound intensity threshold range; and   when the number of reflected ultrasound waves is within a count threshold range, determining that the grid is a grid-to-be-processed.   
     
     
         6 . The detection method of  claim 4 , wherein the floor sensor is an infrared sensor, and the floor sensor signal is a reflected infrared light,
 wherein detecting the grid through a floor sensor on the autonomous mobile device to obtain a floor sensor signal corresponding to the grid comprises:
 transmitting, through the infrared sensor, a plurality of infrared pulses toward the grid and receiving a plurality of reflected infrared lights; and 
   wherein determining that the grid is a grid-to-be-processed based on the floor sensor signal comprises:
 counting, in the plurality of reflected infrared lights, a number of reflected infrared lights having a light intensity within a predetermined light intensity threshold range; and 
 when the number of reflected infrared lights is within a predetermined count threshold range, determining that the grid is a grid-to-be-processed. 
   
     
     
         7 . The detection method of  claim 4 , wherein the floor sensor is a video sensor, and the floor sensor signal is a video frame,
 wherein detecting the grid through a floor sensor on the autonomous mobile device to obtain a floor sensor signal corresponding to the grid comprises:
 capturing, through the video sensor, a video frame of a floor corresponding to the grid; and 
   wherein determining that the grid is a grid-to-be-processed based on the floor sensor signal comprises:
 determining that the grid is a grid-to-be-processed based on the video frame. 
   
     
     
         8 . The detection method of  claim 1 , further comprising:
 when the number of grids-to-be-processed included in the group of grids-to-be-processed is greater than a maximum value of the predetermined range, determining that the group of grids-to-be-processed corresponds to a carpet; and/or   when the number of grids-to-be-processed included in the group of grids-to-be-processed is lower than a minimum value of the predetermined range, determining that the group of grids-to-be-processed corresponds to a lower base of an object in the environment.   
     
     
         9 . The detection method of  claim 2 , wherein
 the environmental map is a light detection and ranging map;   the environmental information is obstacle information in the light detection and ranging map of the environment; and   determining whether the group of grids-to-be-processed corresponds to a doorsill based on the environmental information comprises:
 calculating a location of a center of the group of grids-to-be-processed in the environmental map; 
 based on the location, obtaining a map portion that includes the location from the environmental map; and 
 when the map portion includes obstacle information indicating a door or a hallway, determining that the group of grids-to-be-processed corresponds to the doorsill. 
   
     
     
         10 . The detection method of  claim 1 , wherein, after determining that the group of grids-to-be-processed corresponds to the doorsill, the method also comprises at least one of:
 labelling the doorsill in the environmental map; or   in response to a determination that skidding occurred to the autonomous mobile device and a distance from a current location of the autonomous mobile device to the doorsill is smaller than or equal to a predetermined distance value, controlling the autonomous mobile device to perform predetermined predicament avoidance actions.   
     
     
         11 . An autonomous mobile device, comprising:
 a motion assembly configured to move the autonomous mobile device in an environment;   a storage device configured to store computer-executable instructions; and   a processor configured to retrieve and execute the computer-executable instructions to:
 obtain an environmental map of an environment in which the autonomous mobile device is located, the environmental map being a grid map; 
 obtain grids-to-be-processed in the environmental map; 
 cluster the grids-to-be-processed to obtain one or more groups of grids-to-be-processed; and 
 for each group of grids-to-be-processed, when a number of grids-to-be-processed included in the group of grids-to-be-processed is within a predetermined range, determine whether the group of grids-to-be-processed corresponds to a doorsill. 
   
     
     
         12 . The autonomous mobile device of  claim 11 , wherein when the processor determines whether the group of grids-to-be-processed corresponds to a doorsill, the processor is configured to determine whether the group of grids-to-be-processed corresponds to a doorsill based on environmental information. 
     
     
         13 . The autonomous mobile device of  claim 12 , wherein the environmental information includes at least one of:
 obstacle information in a light detection and ranging map of the environment;   at least portion of video information of the environment; or   a structured light scanning result of the environment.   
     
     
         14 . The autonomous mobile device of  claim 11 , further comprising a floor sensor,
 wherein for each grid of a plurality of grids traversed by the autonomous mobile device when moving in the environment:
 the floor sensor is configured to detect the grid and to generate a floor sensor signal corresponding to the grid; and 
 the processor is configured to determine that the grid is a grid-to-be-processed based on the floor sensor signal. 
   
     
     
         15 . The autonomous mobile device of  claim 14 , wherein the floor sensor is an ultrasound sensor, the floor sensor signal is a reflected ultrasound wave,
 wherein the ultrasound sensor is configured to transmit a plurality of ultrasound pulses toward the grid and receive a plurality of reflected ultrasound wave; and   wherein the processor is configured to:
 count, in the plurality of reflected ultrasound waves, a number of reflected ultrasound waves having a reflected wave intensity within a predetermined ultrasound intensity threshold range; and 
 when the number of reflected ultrasound waves is within a count threshold range, determine that the grid is a grid-to-be-processed. 
   
     
     
         16 . The autonomous mobile device of  claim 14 , wherein the floor sensor is an infrared sensor, and the floor sensor signal is a reflected infrared light,
 wherein the infrared sensor is configured to transmit a plurality of infrared pulses toward the grid and receive a plurality of reflected infrared lights; and   wherein the processor is configured to:
 count, in the plurality of reflected infrared lights, a number of reflected infrared lights having a light intensity within a predetermined light intensity threshold range; and 
 when the number of reflected infrared lights is within a predetermined count threshold range, determine that the grid is a grid-to-be-processed. 
   
     
     
         17 . The autonomous mobile device of  claim 14 , wherein the floor sensor is a video sensor, and the floor sensor signal is a video frame,
 wherein the video sensor is configured to capture a video frame of a floor corresponding to the grid; and   wherein the processor is configured to determine that the grid is a grid-to-be-processed based on the video frame.   
     
     
         18 . The autonomous mobile device of  claim 11 , wherein the processor is configured to:
 when the number of grids-to-be-processed included in the group of grids-to-be-processed is greater than a maximum value of the predetermined range, determine that the group of grids-to-be-processed corresponds to a carpet; and/or   when the number of grids-to-be-processed included in the group of grids-to-be-processed is lower than a minimum value of the predetermined range, determine that the group of grids-to-be-processed corresponds to a lower base of an object in the environment.   
     
     
         19 . The autonomous mobile device of  claim 12 , wherein
 the environmental map is a light detection and ranging map;   the environmental information is obstacle information in the light detection and ranging map of the environment; and   wherein the processor is also configured to:
 calculate a location of a center of the group of grids-to-be-processed in the environmental map; 
 based on the location, obtain a map portion that includes the location from the environmental map; and 
 when the map portion includes obstacle information indicating a door or a hallway, determine that the group of grids-to-be-processed corresponds to the doorsill. 
   
     
     
         20 . A non-transitory computer-readable storage medium storing computer-executable instructions, which when executed by a processor of an autonomous mobile device, cause the autonomous mobile device to perform a detection method comprising:
 obtaining an environmental map of an environment in which the autonomous mobile device is located, the environmental map being a grid map;   obtaining grids-to-be-processed in the environmental map;   clustering the grids-to-be-processed to obtain one or more groups of grids-to-be-processed; and   for each group of grids-to-be-processed, when a number of grids-to-be-processed included in the group of grids-to-be-processed is within a predetermined range, determining whether the group of grids-to-be-processed corresponds to a doorsill.