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US 2026/0223761 A1Published application
Method for Differentiating Dust from Non-Dust Objects in Point Cloud Data
AssigneeAutonomous Solutions, Inc.
InventorTaylor Bybee
Life of this patent
Estimating the term…
Abstract
Disclosed are autonomous vehicle systems and methods for distinguishing atmospheric phenomena, such as dust clouds, from other objects within an operating environment. Point cloud data received from LiDAR sensors may be classified based on multiple returns from LiDAR sensor beams. Atmospheric phenomena may be classified based on a mean return number or a mean last return ratio, or other return-based parameters, for example, by comparison to a return threshold or through a classification process, such as a machine learning process. The autonomous vehicle may then be driven according to the classification of the point cloud data.
Claims20
An autonomous vehicle comprising:
- a steering control system for autonomously controlling a driving direction of the autonomous vehicle;
- a speed control system for autonomously controlling a speed of the autonomous vehicle;
- one or more sensors, including a LiDAR sensor;
- one or more processors communicatively coupled with the one or more sensors, the steering control system, and the speed control system;
- and
- one or more computer-readable media having stored thereon instructions that when executed cause the one or more processors to:
- receive point cloud data from the LiDAR sensor in predetermined time packets, wherein the point cloud data comprises data points indicating at least a portion of an operating environment;
- identify a cluster within the point cloud data, wherein the cluster corresponds to an object within the operating environment and wherein at least one of the data points of the cluster results from a multiple return of a LiDAR beam, and wherein each data point corresponds to a return number that indicates the number of returns obtained from a corresponding LiDAR beam;
- calculate a mean return number associated with the cluster based on all returns of the data points within the cluster, wherein the mean return number represents an average of the return numbers for all data points within the cluster,
- determine a cluster classification based on the mean return number, wherein the cluster classification represents an object type of the object within the operating environment corresponding to the cluster, and wherein the cluster classification includes at least atmospheric phenomena comprising one of dust, mist, smoke, rain, snow, or fog;
- and
- instruct a steering control system and a speed control system to drive an autonomous vehicle along a path through the operating environment based on the cluster classification.
9 more elements
A method for classifying point cloud data, the method comprising:
- receiving point cloud data from a LiDAR sensor in predetermined time packets, wherein the point cloud data comprises data points indicating at least a portion of an operating environment;
- identifying a cluster within the point cloud data, wherein the cluster corresponds to an object within the operating environment and wherein at least one of the data points of the cluster results from a multiple return of a LiDAR beam, and wherein each data point corresponds to a return number that indicates the number of returns obtained from a corresponding LiDAR beam;
- calculating a mean return number associated with the cluster based on all returns of the data points within the cluster, wherein the mean return number represents an average of the return numbers for all data points within the cluster;
- determining a cluster classification based on the mean return number, wherein the cluster classification represents an object type of an object within the operating environment;
- and
- instructing a steering control system and a speed control system to drive an autonomous vehicle along a path through the operating environment based on the cluster classification.
3 more elements
An autonomous vehicle comprising:
- a steering control system for autonomously controlling a driving direction of the autonomous vehicle;
- a speed control system for autonomously controlling a speed of the autonomous vehicle;
- one or more sensors, including a LiDAR sensor;
- one or more processors communicatively coupled with the one or more sensors, the steering control system, and the speed control system;
- and
- one or more computer-readable media having stored thereon instructions that when executed cause the one or more processors to:
- receive point cloud data from the LiDAR sensor in predetermined time packets, wherein the point cloud data comprises data points indicating at least a portion of an operating environment;
- identify a cluster within the point cloud data, wherein the cluster corresponds to an object within the operating environment and wherein at least one of the data points of the cluster results from a multiple return of a LiDAR beam, and wherein each data point corresponds to a return number that indicates the number of returns obtained from a corresponding LiDAR beam;
- calculate a mean last return ratio (MLRR) based on the multiple returns of the data points, wherein the MLRR is determined according to a function comprising:
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- wherein p represents a total number of points within the cluster, ri represents a number of returns r at data point j, and ij represents a return index i at data point j;
- determine a cluster classification based on the MLRR, wherein the cluster classification represents an object type of the object within the operating environment;
- and
- instruct a steering control system and a speed control system to drive an autonomous vehicle along a path through the operating environment based on the cluster classification.
39 more elements
Dependencies are inferred from the claim text.