Object reflectivity estimation in a LIDAR system
Methods, devices, systems, and computer program products for estimating object reflectivity in a light detection and ranging (LIDAR) system are disclosed. The method, for example, includes receiving LIDAR data for a plurality of LIDAR scan cycles. The method also includes generating a dataset from the LIDAR data by accumulating the recorded return signals over the plurality of scan cycles. A data feature associated with an object is identified in the dataset, and one or more parameters of the data feature are identified. An estimated reflectivity of the object may then be determined based on the one or more parameters.
1 . A method for estimating object reflectivity in a light detection and ranging (LIDAR) system, the method comprising:
receiving LIDAR data for a plurality of scan cycles, the LIDAR data including recorded return signals;
generating a dataset from the LIDAR data by accumulating the recorded return signals over the plurality of scan cycles;
identifying in the dataset a data feature associated with an object;
identifying one or more parameters of the data feature and fitting a distribution function to the data feature, wherein the fitting of the distribution function to the data feature re-simulates and recovers data lost through saturation of one or more LIDAR sensors of the LIDAR system;
integrating counts of the fitted distribution function to provide an integrated count value; and
determining an estimated reflectivity of the object based on the one or more parameters and the integrated count value.
2 . The method according to claim 1 , wherein the identifying the one or more parameters of the data feature and the determining the estimated reflectivity of the object comprise applying a machine learning model.
3 . The method according to claim 1 , wherein:
the data feature comprises a peak; and
the fitting of the distribution function to the data feature comprises:
identifying a rising edge of the peak, and
fitting a rising edge of the distribution function to the rising edge of the peak.
4 . The method according to claim 1 , wherein the fitting of the distribution function to the data feature comprises:
identifying a dip in accumulated signal counts of the dataset; and
adjusting a width of the distribution function based on a position of the dip in the dataset.
5 . The method according to claim 1 , wherein the distribution function is a gaussian.
6 . The method according to claim 1 , wherein the generating the dataset comprises generating a histogram.
7 . The method according to claim 6 , wherein the fitting of the distribution function to the data feature comprises fitting a shape of the distribution function to the histogram.
8 . A processing device for estimating object reflectivity in a light detection and ranging (LIDAR) system, the processing device comprising:
an input configured to receive LIDAR data for a plurality of scan cycles, the LIDAR data including recorded return signals; and
a processor configured to
generate a dataset from the LIDAR data by accumulating the recorded return signals over the plurality of scan cycles,
identify in the dataset a data feature associated with an object,
identify one or more parameters of the data feature, and
determine an estimated reflectivity of the object based on the one or more parameters,
wherein
the processor comprises a fitting module,
the fitting module is configured to identify the one or more parameters of the data feature by fitting a distribution function to the data feature,
the fitting of the distribution function to the data feature re-simulates and recovers data lost through saturation of one or more LIDAR sensors of the LIDAR system, and
the processor is configured to determine the estimated reflectivity by integrating counts of the fitted distribution function.
9 . The processing device according to claim 8 , wherein:
the processor comprises a machine learning module; and
the machine learning module is configured to:
identify the one or more parameters of the data feature, and
determine the estimated reflectivity of the object using a machine learning model.
10 . The processing device according to claim 8 , wherein:
the data feature comprises a peak; and
the processor is configured to
identify a rising edge of the peak, and
fit a rising edge of the distribution function to the rising edge of the peak.
11 . The processing device according to claim 8 , wherein the processor is configured to:
identify a dip in accumulated signal counts of the dataset; and
adjust a width of the distribution function based on a position of the dip in the dataset.
12 . The processing device according to claim 8 , wherein the distribution function is a gaussian.
13 . The processing device according to claim 8 , wherein the processor is configured to generate the dataset by generating a histogram.
14 . A non-transitory computer-readable medium for estimating object reflectivity in a light detection and ranging (LIDAR) system, the non-transitory computer-readable medium comprising instructions stored therein that, when executed by a computer, cause the computer to:
receive LIDAR data for a plurality of scan cycles, the LIDAR data including recorded return signals;
generate a dataset from the LIDAR data by accumulating the recorded return signals over the plurality of scan cycles;
identify in the dataset a data feature associated with an object;
identify one or more parameters of the data feature by fitting a distribution function to the data feature, wherein the fitting of the distribution function to the data feature re-simulates and recovers data lost through saturation of one or more LIDAR sensors of the LIDAR system; and
determine an estimated reflectivity of the object based on the one or more parameters by integrating counts of the fitted distribution function.
15 . The non-transitory computer-readable medium according to claim 14 , wherein the instructions, when executed by a computer, cause the computer to identify the one or more parameters of the data feature and the determine the estimated reflectivity of the object by applying a machine learning model.
16 . The method of claim 1 , wherein the estimated reflectivity of the object refers to a rate of photons reflected off the object.
17 . The method of claim 1 , wherein:
the distribution function simulates a linear reflectivity response of the object; and
integrating the counts of the fitted distributed function is performed to recover unsaturated return signals.
18 . The method of claim 1 , wherein:
the fitted distribution function is a Gaussian distribution function;
the integrating of the counts is performed to calculate an area beneath the Gaussian distribution function to obtain a true total accumulated count for photons reflected from the object, wherein the integrated count value is the true total accumulated count; and
the true total accumulated count indicating the reflectivity of the object.
19 . The method of claim 18 , wherein the true total accumulated count for the Gaussian distribution function is greater than an accumulated histogram count of the dataset.