Method and device for classifying sensor data

A light detection and ranging (LiDAR) device, comprising:

• at least one solid state LiDAR sensor configured to generate a point cloud data representing an environment;
• a pre-processing unit to detect a cluster of reflections in the point cloud data;
• a memory storing an averaged feature vector and a threshold value for each class of a multiplicity of predefined classes, wherein, for each class, the averaged feature vector is determined by averaging feature vectors of first training samples of the class, and wherein the threshold value is determined by:
• determining a cosine similarity between the feature vector of each first training sample of the class and the averaged feature vector of the class,
• determining an averaged cosine similarity or a smallest cosine similarity of the first training samples of the class, and
• establishing the averaged cosine similarity or the smallest cosine similarity as the threshold value of the class;
• a classifier unit to determine a feature vector from the cluster of reflections in the point cloud data, to determine a similarity measure between the feature vector and the averaged feature vector for each of the multiplicity of predefined classes, to compare the similarity measure with the threshold value for each of the multiplicity of predefined classes, and to determine that the feature vector is not represented by any of the multiplicity of predefined classes when the similarity measure is not greater than any of the threshold values.

A non-transitory computer-readable storage medium on which a program is stored which enables a computer, after it has been loaded into a memory of the computer, to carry out a method of operating a light detection and ranging (LiDAR) device, the method comprising:

• generating point cloud data representing an environment;
• detecting a cluster of reflections in the point cloud data;
• storing, in a memory, an averaged feature vector and a threshold value for each of a multiplicity of predefined classes, wherein, for each class, the averaged feature vector is determined by averaging feature vectors of first training samples of the class, and wherein the threshold value is determined by:
• determining a cosine similarity between the feature vector of each first training sample of the class and the averaged feature vector of the class,
• determining an averaged cosine similarity or a smallest cosine similarity of the first training samples of the class, and
• establishing the averaged cosine similarity or the smallest cosine similarity as the threshold value of the class;
• determining a feature vector from the cluster of reflections in the point cloud data;
• determining a similarity measure between the feature vector and the averaged feature vector for each of the multiplicity of predefined classes;
• comparing the similarity measure with the threshold value for each of the multiplicity of predefined classes;
• and
• determining that the feature vector is not represented by any of the multiplicity of predefined classes when the similarity measure is not greater than any of the threshold values.