This application claims the benefit of priority to Korean Patent Application No. 10-2015-0090720, filed on Jun. 25, 2015 with the Korean Intellectual Property Office, the disclosure of which is incorporated herein in its entirety by reference.
The present disclosure relates to a system and a method for writing an occupancy grid map of a sensor centered coordinate system using a laser scanner, and more particularly, to a technology for generating an occupancy grid map which is written in a sensor centered coordinate system using a laser scanner.
Laser technology is used for various purposes such as medical treatments, machining, precision measurement, industrial control, imaging, lighting, and the arts.
In particular, since a ray of a laser is focused, the laser may illuminate only a specific target, and it is possible to calculate the distance to the target by measuring the arrival time of a reflected wave reflected from the target, and three dimension image information including distance information may be obtained.
In order to obtain a three-dimensional image using the laser, the distance information for each pixel should be calculated by discharging a laser pulse for each pixel while performing a two-dimensional scanning of the X-axis and the Y-axis in the direction to be observed, and measuring the reception time of the reflected wave.
Thus, a laser scanner has two key functions including a function of scanning by a two-dimensional mechanical method in the X-axis and the Y-axis and a function of measuring a distance using a reflected wave.
The present disclosure has been made in view of the above problems, and provides a technology for generating an occupancy grid map around a sensor written in a sensor centered coordinate system using a single laser scanner, and provides a system and a method for writing an occupancy grid map of a sensor centered coordinate system using a laser scanner to compensate a loss of displacement which occurs when the occupancy grid map moves and determine whether an object detected by the occupancy grid map is a static object or a dynamic object.
In accordance with an aspect of the present disclosure, a system for writing an occupancy grid map of a sensor centered coordinate system using a laser scanner includes: a data unit configured to include a scan data read by the laser scanner, a past measurement map, and a data relating to a movement of sensor; a mapping unit configured to stochastically combine a current measurement map written from the scan data with a predicted map written by using the past measurement map and the data relating to a movement of sensor; and a static and dynamic object detection unit configured to determine whether an object in the occupancy grid map is a static or dynamic object by using a mapping algorithm of the mapping unit. The data relating to a movement of sensor includes speed or yaw information received from a user vehicle. The occupancy grid map is recursively updated by using the scan data which is measured every hour.
In accordance with another aspect of the present disclosure, a method for writing an occupancy grid map of a sensor centered coordinate system using a laser scanner includes: measuring a scan data by using the laser scanner provided to a user vehicle; writing a predicted map which is predicted as a sensor centered coordinate system by using a past measurement map and a data relating to a movement of sensor; writing a current measurement map by using the measured scan data; and writing the occupancy grid map of a sensor centered coordinate system by stochastically combining the written current measurement map with the written predicted map.
Writing a predicted map includes: calculating a displacement of the user vehicle by using the data relating to a movement of sensor; and calculating a significant displacement by adding the displacement of the user vehicle to a past surplus displacement, and calculating a current surplus displacement. The data relating to a movement of sensor includes speed or yaw information received from the user vehicle. After writing the occupancy grid map of a sensor centered coordinate system, the method further includes determining whether an object in the occupancy grid map is a static or dynamic object. The occupancy grid map is recursively updated by using the scan data which is measured every hour.
The present technology is a technology for generating an occupancy grid map around a sensor written in a sensor centered coordinate system using a single laser scanner.
In addition, the present technology compensates a loss of displacement which occurs when the occupancy grid map moves, thereby reducing a discretization error, and solving the inconsistency of a map.
In addition, it is possible to determine a static object or a dynamic object by using an occupancy grid map written in a sensor centered coordinate system.
The objects, features and advantages of the present disclosure will be more apparent from the following detailed description in conjunction with the accompanying drawings, in which:
Exemplary embodiments of the present disclosure are described with reference to the accompanying drawings in detail. The same reference numbers are used throughout the drawings to refer to the same or like parts. Detailed descriptions of well-known functions and structures incorporated herein may be omitted to avoid obscuring the subject matter of the present disclosure.
Referring to
First, the scan data 101 may be data read by the laser scanner.
Next, the past measurement map 102 may mean map information, which was measured in the past, that can be expressed in a coordinate system that has a sensor which is always set as an origin.
The data 103 relating to the sensor movement may be information including a motion data of the sensor.
Here, the occupancy grid map of a sensor centered coordinate system using a laser scanner may be written by using the scan data 101, the past measurement map 102, the data 103 relating to a movement of sensor, and the current measurement map 120. Here, the current measurement map 120 may mean map information, which is currently measured, that can be expressed in a coordinate system that has a sensor which is always set as an origin.
The occupancy grid map may be recursively updated to the latest information by using scan data which is measured every hour.
In detail, the scan data, the past measurement map, and the data relating to a movement of a sensor may be mapped by a mapping algorithm, and the mapped information may be compared with the current measurement map to write the occupancy grid map. The system that writes the occupancy grid map of a sensor centered coordinate system determines whether the object in the occupancy grid map is a static object or a dynamic object.
Referring to
The predicted map 200 may be stochastically combined with the current measurement map 120 to write an occupancy grid map (Map t) 210 of a sensor centered coordinate system at the current time.
Here, in the process of writing an occupancy grid map 210 by stochastically combining the predicted map 200 with the current measurement map 120, the occupancy probability of i-th grid mt,i on a current occupancy grid map mt in a sensor centered coordinate system at a time (t) may be expressed as shown in Equation 1. Here, u1:t represents movement information of the sensor from time 1 to time t, z1:t represents map measurement information from time 1 to time t.
p(mt,i|z1:t,u1:t) [Equation 1]
In addition, in the process of writing an occupancy grid map 210 by stochastically combining the predicted map 200 with the current measurement map 120, the occupancy probability of i-th grid mt,i on a changed predicted map mt in a sensor centered coordinate system from a time (t−1) to a time (t) may be expressed as shown in Equation 2.
p(
In addition, the occupancy probability of a current occupancy grid map in a sensor centered coordinate system at a time (t) may be expressed as shown in Equation 3, and may be updated.
Here, k is a parameter representing uncertainty caused by the movement of the sensor centered coordinate system. Further, k is set close to 1 as the uncertainty is increased, and k is set close to 0 as the uncertainty is decreased. In addition, mt,i represents the i-th grid which is not occupied on the occupancy grid map mt.
The coordinate system of the predicted map mt at time (t) in the occupancy grid map of a sensor centered coordinate system moves as much as the displacement of the sensor in the coordinate system of the predicted map mt-1 at time (t−1). The i-th grid
Referring to
A displacement t 300 of a user vehicle may be calculated by using motion information t 103 including speed or yaw information received from the user vehicle, and a past surplus displacement t−1 310 may be added to be separated into a significant displacement t 320 and a current surplus displacement t 330. Here, the significant displacement 320 may indicate the size of the grid on the coordinate system of the map or the sensor centered coordinate system as a unit.
For example, if the probability of the grid on the map exceeds 0.5, it is determined that the point in which data is generated from the sensor is occupied based on the probability of the grid on the map, and if the vehicle is moved 1.3 in the size of the grid on the map, the significant displacement 320 may be represented as 1, and the current surplus displacement 330 may be represented as 0.3. When the vehicle moves to the next grid from the grid on the map, an algorithm in which the current surplus displacement 330 0.3 becomes the past surplus displacement 310 0.3 to be applied to an operation is performed.
In
This significant displacement and the surplus displacement may be calculated as shown in Equation 4.
Here, Δxt denotes a displacement, Δxt, surplus denotes a surplus displacement, Δxt, significant denotes a significant displacement, mod denotes remaining functions, and grid size denotes a grid structure.
Equation 1 can be calculated under the condition of Δxt-1,surplus+Δxt<grid size/2, Equation 2 can be calculated under the condition of Δxt-1,surplus+Δxt<grid size/2 and, Δxt-1,surplus+Δxt<−grid size/2 and Equation 3 can be calculated under the condition of Δxt-1,surplus+Δxt<−grid size/2.
In addition, the method of recognizing a static or dynamic object using a laser scanner can measure the degree of occupation when the position of scan data is occupied on the current measurement map, and also on the past measurement map. For example, the staticity, or a measure of how static an object is, of the static object may be calculated as shown in Equation 5.
Referring to
Referring to
Here, the occupancy grid map represents an area e in which an object or an obstacle does not exist, a data f that the laser scanner continuously senses, an area g in which the object or the obstacle exists, and an area h in which is impossible to determine whether the object or the obstacle exist.
In addition, the dynamic object in the occupancy grid map is represented as A and A′, and the static object is represented as B and B′.
As described above, the present technology is a technology for generating an occupancy grid map around a sensor written in a sensor centered coordinate system using a single laser scanner.
In addition, the present technology compensates for a loss of displacement which occurs when the occupancy grid map moves, thereby reducing a discretization error, and solving an inconsistency of a map.
Although exemplary embodiments of the present disclosure have been described in detail hereinabove, it should be clearly understood that many variations and modifications of the basic inventive concepts herein taught which may appear to those skilled in the present art will still fall within the spirit and scope of the present disclosure, as defined in the appended claims.
Number | Date | Country | Kind |
---|---|---|---|
10-2015-0090720 | Jun 2015 | KR | national |
Number | Name | Date | Kind |
---|---|---|---|
7142150 | Thackray | Nov 2006 | B2 |
7211980 | Bruemmer | May 2007 | B1 |
8386081 | Landry | Feb 2013 | B2 |
8428778 | Landry | Apr 2013 | B2 |
8798840 | Fong | Aug 2014 | B2 |
20040013295 | Sabe | Jan 2004 | A1 |
20050131581 | Sabe | Jun 2005 | A1 |
20050182518 | Karlsson | Aug 2005 | A1 |
20050234679 | Karlsson | Oct 2005 | A1 |
20080009970 | Bruemmer | Jan 2008 | A1 |
20080027591 | Lenser | Jan 2008 | A1 |
20080046125 | Myeong | Feb 2008 | A1 |
20090234499 | Nielsen | Sep 2009 | A1 |
20100121488 | Lee et al. | May 2010 | A1 |
20100277309 | Anderson | Nov 2010 | A1 |
20110047338 | Stahlin | Feb 2011 | A1 |
20110054689 | Nielsen | Mar 2011 | A1 |
20110082585 | Sofman | Apr 2011 | A1 |
20120053755 | Takagi | Mar 2012 | A1 |
20120239191 | Versteeg | Sep 2012 | A1 |
20140122409 | Na | May 2014 | A1 |
20140129027 | Schnittman | May 2014 | A1 |
20140379256 | Stipes | Dec 2014 | A1 |
20160378111 | Lenser | Dec 2016 | A1 |
Number | Date | Country |
---|---|---|
2003-098256 | Apr 2003 | JP |
2004-062380 | Feb 2004 | JP |
2012-123471 | Jun 2012 | JP |
10-2009-0010367 | Jan 2009 | KR |
10-2009-0078208 | Jul 2009 | KR |
10-2012-0091937 | Aug 2012 | KR |
10-2013-0102873 | Sep 2013 | KR |
10-2014-0054763 | May 2014 | KR |
10-1409323 | Jul 2014 | KR |
Entry |
---|
Thorsten Weiss et al., “Robust Driving Path Detection in Urban and Highway Scenarios Using a Laser Scanner and Online Occupancy Grids”, Proceedings of the 2007 IEEE Intelligent Vehicles Symposium Istanbul, Turkey, Jun. 13-15, 2007. |
Korean Notice of Allowance dated Mar. 22, 2017, issued in Korean Application No. 10-2015-0090720. |
Number | Date | Country | |
---|---|---|---|
20160378115 A1 | Dec 2016 | US |