A system and method for fusing the outputs from multiple LiDAR sensors. The method includes providing object files for objects detected by the sensors at a previous sample time, where the object files identify the position, orientation and velocity of the detected objects. The method also includes receiving a plurality of scan returns from objects detected in the field-of-view of the sensors at a current sample time and constructing a point cloud from the scan returns. The method then segments the scan points in the point cloud into predicted clusters, where each cluster initially identifies an object detected by the sensors. The method matches the predicted clusters with predicted object models generated from objects being tracked during the previous sample time. The method creates new object models, deletes dying object models and updates the object files based on the object models for the current sample time.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for fusing outputs from multiple LiDAR sensors, said method comprising: providing object files for objects detected by the LiDAR sensors at a previous sample time, said object files having object models that identify a position, orientation and velocity of the objects detected by the sensors; tracking the object models of the objects detected by the sensors; projecting tracked object models in the object files from the previous scan time to provide predicted object models; receiving a plurality of scan returns from objects detected in a field-of-view of the sensors at a current sample time; segmenting the scan returns into predicted scan clusters where each scan cluster initially identifies an object detected by the sensors; matching the predicted scan clusters with the predicted object models; merging predicted object models that have been identified as separate scan clusters in the previous sample time but are now identified as a single scan cluster in the current sample time. 2. The method according to claim 1 wherein segmenting the scan returns includes separating clusters of scan points so that the clusters identify a separate object that is being tracked. 3. The method according to claim 1 wherein matching the predicted scan clusters with the predicted object models includes generating a bipartite graph that matches the scan clusters to the predicted object models. 4. The method according to claim 1 wherein generating a bipartite graph includes positioning the scan points at vertices of the graph. 5. The method according to claim 1 wherein merging predicted object models includes converting the bipartite graph to an induced bipartite graph showing the object models that are being merged. 6. The method according to claim 5 wherein converting the bipartite graph to an induced bipartite graph includes computing weights and a cardinality of edges for the induced bipartite graph, where each weight identifies a positional change of one of a scan points for the at least one sensor to a location of a scan point for another one of the sensors and the edges define matching between object model points and segmented scan points. 7. The method according to claim 6 wherein converting the bipartite graph to an induced bipartite graph includes comparing the cardinality of the edges to a threshold and highlighting the edge if the cardinality is greater than the threshold, and wherein an edge is removed if it not highlighted. 8. The method according to claim 1 wherein the LiDAR sensors are on a vehicle. 9. A method for fusing outputs from multiple LiDAR sensors, said method comprising: providing object files for objects detected by the LiDAR sensors at a previous sample time, said object files having object models that identify a position, orientation and velocity of the objects detected by the sensors; tracking the object models of the objects detected by the sensors; projecting tracked object models in the object files from the previous scan time to provide predicted object models; receiving a plurality of scan returns from objects detected in a field-of-view of the sensors at a current sample time; segmenting the scan returns into predicted scan clusters where each scan cluster initially identifies an object detected by the sensors; matching the predicted scan clusters with the predicted object models; and splitting predicted object models that have been identified as a single scan cluster in the previous sample time but are now identified as separate scan clusters in the current sample time. 10. The method according to claim 9 wherein segmenting the return scans includes separating the clusters of scan points in the point cloud so that the clusters identify a separate object that is being tracked. 11. The method according to claim 10 wherein matching the predicted scan clusters with the predicted object models includes generating a bipartite graph that matches the scan clusters to the predicted object models. 12. The method according to claim 11 wherein generating a bipartite graph includes positioning scan points at vertices of the graph. 13. The method according to claim 12 wherein splitting predicted object models includes converting the bipartite graph to an induced bipartite graph showing the object models that are being split. 14. The method according to claim 13 wherein converting the bipartite graph to an induced bipartite graph includes computing weights and a cardinality of edges for the induced bipartite graph, where each weight identifies a positional change of one of a scan points for the at least one sensor to a location of a scan point for another one of the sensors and the edges define matching between object model points and segmented scan points. 15. The method according to claim 14 wherein converting the bipartite graph to an induced bipartite graph includes comparing the cardinality of the edges to a threshold and highlighting the edge if the cardinality is greater than the threshold, and wherein an edge is removed if it not highlighted. 16. The method according to claim 9 wherein the LiDAR sensors are on a vehicle. 17. A system for fusing outputs from multiple LiDAR sensors, said system comprising: a plurality of LiDAR sensors configured to detect objects; and a processor in communication with the LIDAR sensors and being configured for: providing object files for the objects detected by the LiDAR sensors at a previous sample time, said object files having object models that identify a position, orientation and velocity of the objects detected by the sensors; tracking the object models of the objects detected by the sensors; projecting tracked object models in the object files from the previous scan time to provide predicted object models; receiving a plurality of scan returns from objects detected in a field-of-view of the sensors at a current sample time; constructing a scan point cloud from the scan returns; segmenting the scan points in the point cloud into predicted scan clusters where each scan cluster initially identifies an object detected by the sensors; matching the predicted scan clusters with the predicted object models; merging predicted object models that have been identified as separate scan clusters in the previous sample time but are now identified as a single scan cluster in the current sample time; splitting predicted object models that have been identified as a single scan cluster in the previous sample time but are now identified as separate scan clusters in the current sample time; creating new object models for detected objects in the current sample time that were not present in the previous sample time; deleting object models that are no longer present in the predicted scan clusters; providing object model updates based on the merged object models, split object models, new object models and deleted object models; and updating the object files for the current sample time with the object model updates. 18. The system according to claim 17 wherein matching the predicted scan clusters with the predicted object models generates a bipartite graph that matches the scan clusters to the predicted object models. 19. The system according to claim 17 wherein merging predicted object models and splitting predicted object models converts the bipartite graph to an induced bipartite graph showing the object models that are being merged and split. 20. The system according to claim 19 wherein converting the bipartite graph to an induced bipartite graph includes computing weights and a cardinality of edges for the induced bipartite graph, where each weight identifies a positional change of one of a scan points for the at least one sensor to a location of a scan point for another one of the sensors and the edges define matching between object model points and segmented scan points.