Curb detection by analysis of reflection images
Disclosed are devices, systems and methods for curb detection using light detection and ranging (LiDAR) sensors. One example of a method for curb detection includes acquiring, based on a scan of an area around a vehicle, a point-cloud frame that includes a description of an intensity of a reflection of the area around the vehicle, selecting a subset of points from the point-cloud frame, seeding each of a plurality of clusters based on the selected points, determining a criterion for each of the selected points, increasing a size of each of the plurality of clusters by including additional points from the point-cloud frame that meet the criterion for the corresponding cluster, identifying a largest cluster of the plurality of clusters upon completion of the increasing step, and detecting a curb based on a boundary of the largest cluster.
1. A method for detecting road features, comprising:
acquiring, based on a scan of an area around a vehicle, a point-cloud frame that comprises a description of an intensity of a reflection of the area around the vehicle;
selecting a subset of points from the point-cloud frame;
seeding each of a plurality of clusters based on the selected points;
determining a criterion for each of the selected points;
increasing a size of each of the plurality of clusters by including additional points from the point-cloud frame that meet the criterion for the corresponding cluster;
identifying, upon completion of the increasing step, a largest cluster of the plurality of clusters; and
detecting a curb based on a boundary of the largest cluster.
2. The method of claim 1 , wherein the criterion is at least one feature of each of the additional points meeting a threshold associated with the corresponding cluster.
3. The method of claim 2 , wherein the at least one feature comprises a normal vector at each of the additional points and a curvature of a subset of the additional points.
4. The method of claim 3 , wherein the curvature is computed as λ 0 /(λ 0 +λ 1 +λ 2 ), where λ 0 <λ 1 <λ 2 are eigenvalues of a covariance matrix based on the subset of the additional points.
5. The method of claim 1 , wherein the point-cloud frame is an accumulated point-cloud, and wherein acquiring the point-cloud frame comprises:
scanning the area around the vehicle using a light detection and ranging (LiDAR) sensor;
generating, based on the scanning step, a plurality of single-frame point-clouds; and
performing an accumulation operation on the plurality of single-frame point-clouds to generate the point-cloud frame.
6. The method of claim 5 , wherein generating the plurality of single-frame point-clouds is further based on a Global Positioning System (GPS) sensor and an inertial measurement unit (IMU), and wherein each of the plurality of single-frame point-clouds are generated in a common coordinate system.
7. The method of claim 5 , wherein the scanning is based on one or more of a rotation rate parameter, an image resolution parameter and a frame rate parameter.
8. The method of claim 1 , wherein the point-cloud frame further comprises a three-dimensional geometry of the area around the vehicle, and wherein each point in the point-cloud frame comprises (x,y,z) coordinates and an intensity value.
9. The method of claim 1 , further comprising:
smoothing the boundary of the largest cluster based on a spline function.
10. The method of claim 1 , wherein detecting the curb is further based on a concave hull of the largest cluster.
11. The method of claim 1 , further comprising:
determining whether any of the plurality of clusters comprises a bounded region of points, wherein each point of the bounded region of points does not meet the criterion; and
adding the points of the bounded region of points to the corresponding cluster.
12. An apparatus for detecting road features, comprising:
a processor; and
a memory comprising processor executable code, the processor executable code when executed by the processor causes the processor to:
acquire, based on a scan of an area around a vehicle, a point-cloud frame that comprises a description of an intensity of a reflection of the area around the vehicle;
select a subset of points from the point-cloud frame;
seed each of a plurality of clusters based on the selected points;
determine a criterion for each of the selected points;
increase a size of each of the plurality of clusters by including additional points from the point-cloud frame that meet the criterion for the corresponding cluster;
identify, upon completion of the increasing step, a largest cluster of the plurality of clusters; and
detect a curb based on a boundary of the largest cluster.
13. The apparatus of claim 12 , further comprising:
a light detection and ranging (LiDAR) sensor configured to generate the scan of the area around the vehicle, wherein the processor executable code when executed by the processor further causes the processor to:
generate, based on the scanning step, a plurality of single-frame point-clouds; and
perform an accumulation operation on the plurality of single-frame point-clouds to generate the point-cloud frame.
14. The apparatus of claim 13 , further comprising:
a Global Positioning System (GPS) sensor; and
an inertial measurement unit (IMU),
wherein generating the plurality of single-frame point-clouds is further based on the GPS sensor and the IMU, and wherein each of the plurality of single-frame point-clouds are generated in a common coordinate system.
15. The apparatus of claim 12 , wherein the point-cloud frame further comprises a three-dimensional geometry of the area around the vehicle, and wherein each point in the point-cloud frame comprises (x,y,z) coordinates and an intensity value.
16. The apparatus of claim 12 , wherein the processor executable code when executed by the processor further causes the processor to:
determine whether any of the plurality of clusters comprises a bounded region of points, wherein each point of the bounded region of points does not meet the criterion; and
add the points of the bounded region of points to the corresponding cluster.
17. A non-transitory computer-readable medium storing a program causing a computer to execute a process, the process comprising:
acquiring, based on a scan of an area around a vehicle, a point-cloud frame that comprises a description of an intensity of a reflection of the area around the vehicle;
selecting a subset of points from the point-cloud frame;
seeding each of a plurality of clusters based on the selected points;
determining a criterion for each of the selected points;
increasing a size of each of the plurality of clusters by including additional points from the point-cloud frame that meet the criterion for the corresponding cluster;
identifying, upon completion of the increasing step, a largest cluster of the plurality of clusters; and
detecting a curb based on a boundary of the largest cluster.
18. The non-transitory computer-readable medium of claim 17 , wherein the criterion is at least one feature of each of the additional points meeting a threshold associated with the corresponding cluster.
19. The non-transitory computer-readable medium of claim 18 , wherein the at least one feature comprises a normal vector at each of the additional points and a curvature of a subset of the additional points.
20. The non-transitory computer-readable medium of claim 17 , wherein the point-cloud frame is an accumulated point-cloud, and wherein acquiring the point-cloud frame comprises:
scanning the area around the vehicle using a light detection and ranging (LiDAR) sensor;
generating, based on the scanning step, a plurality of single-frame point-clouds; and
performing an accumulation operation on the plurality of single-frame point-clouds to generate the point-cloud frame.