Determining extrinsic calibration parameters for a sensor

{
  "publication_number": "US2014240690A1",
  "country": "US",
  "kind": "A1",
  "title": "Determining extrinsic calibration parameters for a sensor",
  "abstract": "A method of determining extrinsic calibration parameters for at least one sensor (102, 104, 106) mounted on transportable apparatus (100). The method includes receiving (202) data representing pose history of the transportable apparatus and receiving (202) sensor data from at least one sensor mounted on transportable apparatus. The method generates (204) at least one point cloud data using the sensor data received from the at least one sensor, each point in a said point cloud having a point covariance derived from the pose history data. The method then maximises (206) a value of a quality function for the at least one point cloud, and uses (208) the maximised quality function to determine extrinsic calibration parameters for the at least one sensor.",
  "claims": [
    "1. A method of determining extrinsic calibration parameters for at least one sensor mounted on transportable apparatus, the method including: receiving data representing pose history of the transportable apparatus; receiving sensor data from at least one sensor mounted on transportable apparatus; generating at least one point cloud datum using the sensor data received from the at least one sensor, each point in a said point cloud having a point covariance derived from the pose history data; maximising a value of a quality function for the at least one point cloud; and using the maximised quality function to determine extrinsic calibration parameters for the at least one sensor. 2. A method according to claim 1, wherein the value of the quality function depends on pairwise distances between said points in a said point cloud (X̂) and associated said point covariance (Σ) of each said point derived from the pose history data. 3. A method according to claim 1, wherein the quality function (E) is expressed as E(θ|Z, Y), where Z represents the data received from one said sensor, the sensor data representing a set of measurements over a period of time, and where Y represents a set of poses of the transportable apparatus from the pose history over the period of time, and where θ provides a most likely estimate for the extrinsic calibration parameters for the sensor. 4. A method according to claim 1, wherein the quality function comprises an entropy-based point cloud quality metric. 5. A method according to claim 4, wherein the quality function is based on Renyi Quadratic Entropy. 6. A method according to claim 1, wherein the maximising of the value of the quality function is performed using Newton's method. 7. A method according to claim 1, wherein the method is used to: generate a first point cloud based on sensor data received from a 3D LIDAR device; generate a second point cloud based on sensor data received from a 2D LIDAR device, and use a Kernelized Renyi Distance function on the first and the second point clouds to determine the extrinsic calibration parameters for the 2D LIDAR device. 8. A method according to claim 1, where, in the step of maximising the value of a quality function for the point cloud, the method evaluates bi-direction data only once to reduce computation time. 9. A method according to claim 1, where, in the step of maximising the value of a quality function for the point cloud, the method evaluates an entropy contribution between a first said point of the point cloud and neighbouring said points in the point cloud to reduce computation time. 10. A method according to claim 1, wherein the point cloud data is stored in a tree data structure for processing. 11. A method according to claim 10, wherein the tree data structure comprises a k-d tree. 12. Transportable apparatus including at least one sensor and a processor configured to execute a method according to claim 1. 13. A vehicle including transportable apparatus according to claim 12. 14. (canceled) 15. A method of calibrating at least one sensor mounted on transportable apparatus, the method including: receiving data representing pose history of the transportable apparatus; receiving sensor data from at least one sensor mounted on transportable apparatus; generating at least one point cloud datum using the sensor data received from the at least one sensor, each point in a said point cloud having a point covariance derived from the pose history data; maximising a value of a quality function for the at least one point cloud; using the maximised quality function to determine extrinsic calibration parameters for the at least one sensor, and calibrating the at least one sensor using the determined extrinsic calibration parameters. 16. A non-transient computer program product comprising computer code that when executed by one or more processors causes a process for determining extrinsic calibration parameters for at least one sensor mounted on transportable apparatus, the process comprising: receiving data representing pose history of the transportable apparatus; receiving sensor data from at least one sensor mounted on transportable apparatus; generating at least one point cloud datum using the sensor data received from the at least one sensor, each point in a said point cloud having a point covariance derived from the pose history data; maximising a value of a quality function for the at least one point cloud; and using the maximised quality function to determine extrinsic calibration parameters for the at least one sensor. 17. A computer program product according to claim 16, wherein the value of the quality function depends on pairwise distances between said points in a said point cloud (X̂) and associated said point covariance (Σ) of each said point derived from the pose history data. 18. A computer program product according to claim 16, wherein the quality function (E) is expressed as E(θ|Z, Y), where Z represents the data received from one said sensor, the sensor data representing a set of measurements over a period of time, and where Y represents a set of poses of the transportable apparatus from the pose history over the period of time, and where θ provides a most likely estimate for the extrinsic calibration parameters for the sensor. 19. A computer program product according to claim 16, wherein the quality function comprises an entropy-based point cloud quality metric and the maximising of the value of the quality function is performed using Newton's method. 20. A computer program product according to claim 16, wherein the process is used to: generate a first point cloud based on sensor data received from a 3D LIDAR device; generate a second point cloud based on sensor data received from a 2D LIDAR device, and use a Kernelized Renyi Distance function on the first and the second point clouds to determine the extrinsic calibration parameters for the 2D LIDAR device. 21. A computer program product according to claim 16, wherein the point cloud data is stored in a tree data structure for processing, and the tree data structure comprises a k-d tree."
  ],
  "cpc": [
    "G01S 17/02",
    "G01S 17/87",
    "G01S 17/875",
    "G01S 17/89",
    "G01S 7/4808",
    "G01S 7/4972"
  ],
  "assignees": [
    "NEWMAN PAUL MICHAEL",
    "MADDERN WILLIAM PAUL",
    "HARRISON ALASTAIR ROBIN",
    "SHEEHAN MARK CHRISTOPHER",
    "UNIV OXFORD"
  ],
  "filing_date": "2012-09-27",
  "publication_date": "2014-08-28",
  "priority_date": "2011-09-30",
  "application_number": "US-201214348482-A",
  "family_id": "45035009"
}