Methods and systems for detecting intrusions in a monitored volume

1. A method for detecting intrusions in a monitored volume, in which a plurality of N tridimensional sensors respectively monitor at least a part of a monitored volume and respectively communicate with a central processing unit, comprising:

each sensor of said plurality of N tridimensional sensors acquiring a local point cloud in a local coordinate system of said sensor, said local point cloud comprising a set of tridimensional data points of object surfaces in a local volume surrounding said sensor and overlapping the monitored volume,

said central processing unit receiving the acquired local point clouds from the plurality of N tridimensional sensors, storing said acquired point clouds in a memory and,

for each sensor of said plurality of N tridimensional sensors,

computing updated tridimensional position and orientation of said sensor in a global coordinate system of the monitored volume by aligning a local point cloud acquired by said tridimensional sensor with a global tridimensional map of the monitored volume stored in a memory, and

generating an aligned local point cloud from said acquired point cloud on the basis of the updated tridimensional position and orientation of the sensor, and

monitoring an intrusion in the monitored volume by comparing a free space of said aligned local point cloud with a free space of the global tridimensional map.

2. The method according to claim 1 wherein, for each sensor of said plurality of N tridimensional sensors, the updated tridimensional position and orientation of said sensor in the global coordinate system is computed by performing a simultaneous multi-scan alignment of each point cloud acquired by said sensor with the global tridimensional map of the monitored volume.

3. The method according to claim 1 , wherein the updated tridimensional position and orientation of each sensor of said plurality of N tridimensional sensors is computed only from the local point clouds acquired by said tridimensional sensor and the global tridimensional map of the monitored volume stored in a memory, and without additional positioning information.

4. The method according to claim 1 , wherein the N tridimensional sensors are located so that a union of the local volumes surrounding said sensors is a connected space, said connected space forming the monitored volume,

and wherein the global tridimensional map of the monitored volume is determined by:

receiving at least one local point cloud from each of said plurality of N tridimensional sensors and storing said local point clouds in a memory,

performing a simultaneous multi-scans alignment of the stored local point clouds to generate a plurality of aligned local point clouds respectively associated to the local point clouds acquired from each of said plurality of N tridimensional sensors, and

merging said plurality of aligned local point clouds to determine a global tridimensional map of the monitored volume and storing said global tridimensional map in the memory.

5. The method according to claim 1 , further comprising displaying to a user a graphical indication of the intrusion on a display device.

6. The method according to claim 5 , further comprising generating a bidimensional image of the monitored volume by projecting the global tridimensional map of the monitored volume, and commanding the display device to display the graphical indication of the intrusion overlaid over said bidimensional image of the monitored volume.

7. The method according to claim 5 , wherein the method is for a self-calibrated monitoring system, the method further comprising commanding the display device to display the graphical indication of the intrusion overlaid over a bidimensional image of at least a part of the monitored volume acquired by a camera of the self-calibrated monitoring system.

8. The method according to claim 7 , further comprising orienting the camera of the self-calibrated monitoring system so that the detected intrusion is located in a field of view of the camera.

9. A method for extending a volume monitored by a method according to claim 1 , in which a plurality of N tridimensional sensors respectively monitor at least a part of the monitored volume and respectively communicate with a central processing unit,

comprising:

positioning an additional N+lth tridimensional sensor communicating with the central processing unit, the additional N+lth tridimensional sensor acquiring a local point cloud in a local coordinate system of said sensor, said local point cloud comprising a set of tridimensional data points of object surfaces in a local volume surrounding said sensor and at least partially overlapping the volume monitored by the plurality of N tridimensional sensors,

determining an updated global tridimensional map of a self-calibrated monitoring system by:

receiving at least one local point cloud acquired from each of said plurality of N tridimensional sensors and storing said local point clouds in a memory,

performing a simultaneous multi-scans alignment of the stored local point clouds to generate a plurality of aligned local point clouds respectively associated to the local point clouds acquired from each of said plurality of N tridimensional sensors, and

determining a global tridimensional map of a monitored volume by merging said plurality of aligned local point clouds.

10. A method for determining a tridimensional location of a camera for a self-calibrated monitoring system, in which a plurality of N tridimensional sensors respectively monitor at least a part of a monitored volume and respectively communicate with a central processing unit,

providing the camera, wherein the camera comprises at least one reflective pattern such that a data point of said reflective pattern acquired by a tridimensional sensor of the self-calibrated monitoring system can be associated to said camera in the monitored volume, in a field of view of at least one sensor of the plurality of N tridimensional sensors so that said sensor acquire a local point cloud comprising at least one tridimensional data point of the reflective pattern of the camera,

receiving a local point cloud from said at least one tridimensional sensor and computing an aligned local point cloud by aligning said local point cloud with a global tridimensional map of the self-calibrated monitoring system,

identifying, in the aligned local point cloud at least one data point corresponding to the reflective pattern of the camera, and

determining at least a tridimensional location of the camera in a global coordinate system of the global tridimensional map on the basis of the coordinates of said identified data point of the aligned local point cloud corresponding to the reflective pattern of the camera.

11. A self-calibrated monitoring system for detecting intrusions in a monitored volume, the system comprising:

a plurality of N tridimensional sensors respectively able to monitor at least a part of the monitored volume, each sensor of said plurality of N tridimensional sensors being able to acquire a local point cloud in a local coordinate system of said sensor, said local point cloud comprising a set of tridimensional data points of object surfaces in a local volume surrounding said sensor and overlapping the monitored volume;

a memory to store said local point cloud and a global tridimensional map of a monitored volume comprising a set of tridimensional data points of object surfaces in a monitored volume, the local volume at least partially overlapping the monitored volume,

a central processing unit able to receive the acquired local point clouds from the plurality of N tridimensional sensors, store said acquired point clouds in a memory and,

for each sensor of said plurality of N tridimensional sensors,

compute updated tridimensional position and orientation of said sensor in a global coordinate system of the monitored volume by aligning a local point cloud acquired by said tridimensional sensor with a global tridimensional map of the monitored volume stored in a memory,

generate an aligned local point cloud from said acquired point cloud on the basis of the updated tridimensional position and orientation of the sensor, and

monitor an intrusion in the monitored volume by comparing a free space of said aligned local point cloud with a free space of the global tridimensional map.

12. The monitoring system according to claim 11 , further comprising at least one camera able to acquire a bidimensional image of a portion of the monitored volume.

13. The monitoring system according to claim 12 , wherein said at least one camera comprises at least one reflective pattern such that a data point of said reflective pattern acquired by a tridimensional sensor of the self-calibrated monitoring system can be associated to said camera by the central processing unit of the system.

14. The monitoring system according to claim 11 , further comprising at least one display device able to display to a user a graphical indication of the intrusion.

15. A non-transitory computer readable storage medium, having stored thereon a computer program comprising program instructions, the computer program being loadable into a central processing unit of a monitoring system and adapted to cause the processing unit to carry out the steps of a method when the computer program is run by the central processing unit, the method comprising:

each sensor of a plurality of N tridimensional sensors acquiring a local point cloud in a local coordinate system of said sensor, said local point cloud comprising a set of tridimensional data points of object surfaces in a local volume surrounding said sensor and overlapping a monitored volume,

said central processing unit receiving the acquired local point clouds from the plurality of N tridimensional sensors, storing said acquired point clouds in a memory and,

for each sensor of said plurality of N tridimensional sensors,

computing updated tridimensional position and orientation of said sensor in a global coordinate system of the monitored volume by aligning a local point cloud acquired by said tridimensional sensor with a global tridimensional map of the monitored volume stored in a memory, and

generating an aligned local point cloud from said acquired point cloud on the basis of the updated tridimensional position and orientation of the sensor, and

monitoring an intrusion in the monitored volume by comparing a free space of said aligned local point cloud with a free space of the global tridimensional map.