VEHICLE AND METHOD OF RECOGNIZING POSITION OF VEHICLE BASED ON MAP

• US 20170227647A1
• Filed: 06/14/2016
• Published: 08/10/2017
• Est. Priority Date: 02/05/2016
• Status: Active Grant

• Current Assignee: Samsung Electronics Co. Ltd.
• Original Assignee: Samsung Electronics Co. Ltd.
• Inventors: BAIK, Aron
• Granted Patent: US 10,768,305 B2
• Time in Patent Office: Days
• CPC Class Codes: G01C 21/30 Map- or contour-matching
• CPC Class Codes: G01S 17/06 Systems determining position data of a target
• CPC Class Codes: G01S 17/42 Simultaneous measurement of distance and other co-ordinates indirect measurement G01S17/46
• CPC Class Codes: G01S 17/86 Combinations of lidar systems with systems other than lidar, radar or sonar, e.g. with direction finders
• CPC Class Codes: G01S 17/89 for mapping or imaging
• CPC Class Codes: G01S 17/931 of land vehicles
• CPC Class Codes: G01S 19/42 Determining position
• CPC Class Codes: G01S 19/485 whereby the further system is an optical system or imaging system
• CPC Class Codes: G01S 7/4802 using analysis of echo signal for target characterisation; Target signature; Target cross-section
• CPC Class Codes: G01S 7/4808 Evaluating distance, position or velocity data
• CPC Class Codes: G01S 7/4815 using multiple transmitters

Abstract

A vehicle is provided. The vehicle includes a light detection and ranging (LiDAR) sensor to acquire point cloud information for each channel on a surrounding ground of the vehicle by using a multichannel laser. The vehicle further includes a communicator to communicate with an external server, and a processor to control the communicator to receive map data from the external server and to determine a position of the vehicle in the map data based on the point cloud information for each channel, acquired through the LiDAR sensor.

Claims

1. A vehicle comprising:

a light detection and ranging (LiDAR) sensor configured to acquire point cloud information for each channel on a surrounding ground of the vehicle using a multichannel laser;

a communicator configured to communicate with an external server; and

a processor configured to;

control the communicator to receive map data from the external server and;

determine a position of the vehicle in the map data based on the point cloud information for each channel, acquired through the LiDAR sensor.

2. The vehicle as claimed in claim 1,

wherein the map data is multichannel map data comprising reflectivity information for each channel of the multichannel laser;

and

wherein the processor is further configured to match the point cloud information for each channel, acquired through the LiDAR sensor, and the multichannel map data to determine the position of the vehicle.

3. The vehicle as claimed in claim 1,

wherein the map data is a single-channel map data obtained by synthesizing reflectivity information for each channel of the multichannel laser;

and

wherein the processor is further configured to;

synthesize the point cloud information for each channel acquired through the LiDAR sensor into single-charnel point cloud information, and

match the synthesized single-channel point cloud information and the single-channel map data to determine the position of the vehicle.

4. The vehicle as claimed in claim 3, wherein the processor is further configured to:

multiply reflectivity information included in the point cloud information for each channel by a weight for each channel, and

sum the reflectivity information for each channel, multiplied by the weight, to synthesize the point cloud information for each channel into the single-channel point cloud information.

5. The vehicle as claimed in claim 4,

wherein the processor is further configured to:

calculate variance or standard deviation of the reflectivity information included in the point cloud information for each channel; and

multiply a channel with a greater calculated variance value or standard deviation by a greater weight.

6. The vehicle as claimed in claim 1, wherein the processor is further configured to:

rasterize the point cloud information acquired through the LiDAR sensor into two-dimensional (2D) data;

perform reflected-light removal on the rasterized 2D data; and

match the 2D data with the reflected-light removal performed thereon and the map data to determine the position of the vehicle.

7. The vehicle as claimed in claim 6, further comprising:

an ambient light sensor configured to detect intensity of ambient light around the vehicle,

wherein the processor adjusts a degree for the reflected-light removal according to the intensity of ambient light around the vehicle, detected through the ambient light sensor.

8. The vehicle as claimed in claim 6, further comprising:

a global positioning system (GPS) receiver configured to receive a GPS signal comprising positional information of the vehicle,

wherein the processor is further configured to;

determine a position of the sun based on the GPS signal received through the GPS signal receiver and current time, and

use the determined position of the sun in the reflected-light removal.

9. The vehicle as claimed in claim 1, further comprising:

a global positioning system (GPS) receiver configured to receive a GPS signal comprising positional information of the vehicle,

wherein the processor is further configured to;

determine the position of the vehicle using the GPS signal received through the GPS signal receiver, and

determine a position of the vehicle in the map data based on the position of the vehicle, determined using the GPS signal.

10. The vehicle as claimed in claim 9, wherein the processor is further configured to control the communicator to receive the map data corresponding to a preset distance range from the position of the vehicle, determined using the GPS signal, from the server.

11. A method of recognizing a position of a vehicle based on a map, the method comprising:

receiving map data from an external server;

acquiring point cloud information for each channel on a surrounding ground of the vehicle using a multichannel light detection and ranging (LiDAR) sensor; and

determining a position of the vehicle in the map data based on the point cloud information for each channel.

12. The method as claimed in claim 11,

wherein the map data is multichannel map data comprising reflectivity information for each channel of the multichannel laser;

and

wherein the determining of the position of the vehicle comprises matching the point cloud information for each channel, acquired through the LiDAR sensor, and the multichannel map data to determine the position of the vehicle.

13. The method as claimed in claim 11,

wherein the map data is a single-channel map data obtained by synthesizing reflectivity information for each channel of the multichannel laser;

and

wherein the determining of the position of the vehicle comprises;

synthesizing the point cloud information for each channel acquired through the LiDAR sensor into single-channel point cloud information, and

matching the synthesized single-channel point cloud information and the single-channel map data to determine a position of the vehicle.

14. The method as claimed in claim 13, wherein the synthesizing of the point cloud information comprises:

multiplying reflectivity information included in the point cloud information for each channel by a weight for each channel; and

summing the reflectivity information for each channel, multiplied by the weight.

15. The method as claimed in claim 14, wherein the multiplying of the reflectivity information with the weight comprises:

calculating variance or standard deviation of the reflectivity information included in the point cloud information for each channel, for each channel; and

multiplying a channel with a greater calculated variance value or standard deviation by a greater weight.

16. The method as claimed in claim 11, wherein the determining of the position of the vehicle comprises:

rasterizing the point cloud information acquired through the LiDAR sensor into two-dimensional (2D) data;

performing reflected-light removal on the rasterized 2D data; and

matching the 2D data with the reflected-light removal performed thereon and the map data to determine the position of the vehicle.

17. The method as claimed in claim 16, further comprising:

detecting intensity of ambient light around the vehicle,

wherein the performing of the reflected-light removal comprises adjusting a degree for the reflected-light removal according to the detected intensity of ambient light around the vehicle.

18. The method as claimed in claim 16, further comprising:

receiving a global positioning system (GPS) signal comprising positional information of the vehicle,

wherein the performing of the reflected-light removal comprises;

determining a position of the sun based on the received GPS signal and current time, and

performing the reflected-light removal using the determined position of the sun.

19. The method as claimed in claim 11, further comprising:

receiving a global positioning system (GPS) signal comprising positional information of the vehicle,

wherein the determining of the position of the vehicle comprises;

determining the position of the vehicle using the received GPS signal, and

determining a position of the vehicle in the map data based on the position of the vehicle, determined using the GPS signal, and

wherein the receiving of the map data comprises receiving the map data corresponding to a preset distance range from the position of the vehicle, determined using the GPS signal, from the server.

20. A non-transitory computer-readable storage medium storing instructions that, when executed, cause at least one processor to perform the method of claim 11.