Characterizing optically reflective features via hyper-spectral sensor

An autonomous vehicle system comprising:

• a light detection and ranging (LIDAR) device including:
• a light source configured to be scanned through a scanning zone while emitting light pulses;
• and
• a light detector configured to receive returning reflected signals from reflective features in the scanning zone, if any;
• a hyperspectral sensor configured to image selected regions of the scanning zone and to characterize spectral distributions of radiation received from the imaged regions, wherein the hyperspectral sensor is separate from the LIDAR device;
• and
• a controller configured to:
• cause the LIDAR device to scan the scanning zone while emitting light pulses;
• receive information from the LIDAR device indicative of the time delays between the emission of the light pulses and the reception of corresponding returning reflected signals;
• determine, based on the time delays and orientations of the LIDAR device while emitting the light pulses, a three dimensional (3-D) point map;
• identify a region of the scanning zone for spectral analysis based on the 3-D point map including a particular reflective feature that resembles a type of non-solid object;
• cause the hyperspectral sensor to image the identified region of the scanning zone;
• receive, from the hyperspectral sensor, spectral information characterizing a spectral distribution of radiation received from the particular reflective feature;
• correlate the particular reflective feature with a non-solid material based on the spectral distribution of radiation received from the particular reflective feature including one or more spectral characteristics of the non-solid material;
• in response to the correlating, determining that the particular reflective feature corresponds to a non-solid object;
• combining information from the LIDAR device and the hyperspectral sensor, wherein the combined information indicates whether reflective features in the 3-D point map correspond to solid objects or non-solid objects;
• analyze the combined information to identify obstacles relative to a current path of the autonomous vehicle;
• determine a modified path of the autonomous vehicle that avoids the identified obstacles;
• and
• control the autonomous vehicle to navigate along the modified path.

A computer readable medium storing instructions that, when executed by one or more processors in a computing device, cause the computing device to perform operations, the operations comprising:

• scanning a light detection and ranging (LIDAR) device associated with an autonomous vehicle through a scanning zone while emitting light pulses;
• determining a three-dimensional (3-D) point map of the scanning zone based on time delays between emitting the light pulses and receiving corresponding returning reflected signals and based on orientations of the LIDAR device while emitting the light pulses;
• identifying a region of the scanning zone for spectral analysis based on the 3-D point map including a particular reflective feature that resembles a type of non-solid object;
• imaging, with a hyperspectral sensor associated with the autonomous vehicle, the identified region of the scanning zone so as to generate spectral information characterizing a spectral distribution of radiation received from the particular reflective feature;
• correlating the particular reflective feature with a non-solid material based on the spectral distribution of radiation received from particular reflective feature including one or more spectral characteristics of the non-solid material;
• in response to the correlating, determining that the particular reflective feature corresponds to a non-solid object;
• combining information from the LIDAR device and the hyperspectral sensor, wherein the hyperspectral sensor is separate from the LIDAR device, and wherein the combined information indicates whether reflective features in the 3-D point map correspond to solid objects or non-solid objects;
• analyzing the combined information to identify obstacles relative to a current path of the autonomous vehicle;
• determining a modified path of the autonomous vehicle that avoids the identified obstacles;
• and
• controlling the autonomous vehicle to navigate along the modified path.