Abstract: Vision systems for autonomous machines and methods of using same during machine localization are provided. Exemplary systems and methods may reduce computing resources needed to perform vision-based localization by selecting the most appropriate camera from two or more cameras, and optionally selecting only a portion of the selected camera's field of view, from which to perform vision-based location correction. Other embodiments may provide camera lens coverings that maintain optical clarity while operating within debris-filled environments.

Abstract: Autonomous machine navigation techniques may generate a three-dimensional point cloud that represents at least a work region based on feature data and matching data. Pose data associated with points of the three-dimensional point cloud may be generated that represents poses of an autonomous machine. A boundary may be determined using the pose data for subsequent navigation of the autonomous machine in the work region. Non-vision-based sensor data may be used to determine a pose. The pose may be updated based on the vision-based pose data. The autonomous machine may be navigated within the boundary of the work region based on the updated pose. The three-dimensional point cloud may be generated based on data captured during a touring phase. Boundaries may be generated based on data captured during a mapping phase.

Abstract: Vision systems for autonomous machines and methods of using same during machine localization are provided. Exemplary systems and methods may reduce computing resources needed to perform vision-based localization by selecting the most appropriate camera from two or more cameras, and optionally selecting only a portion of the selected camera's field of view, from which to perform vision-based location correction. Other embodiments may provide camera lens coverings that maintain optical clarity while operating within debris-filled environments.

Abstract: Autonomous machine navigation techniques may generate a three-dimensional point cloud that represents at least a work region based on feature data and matching data. Pose data associated with points of the three-dimensional point cloud may be generated that represents poses of an autonomous machine. A boundary may be determined using the pose data for subsequent navigation of the autonomous machine in the work region. Non-vision-based sensor data may be used to determine a pose. The pose may be updated based on the vision-based pose data. The autonomous machine may be navigated within the boundary of the work region based on the updated pose. The three-dimensional point cloud may be generated based on data captured during a touring phase. Boundaries may be generated based on data captured during a mapping phase.

Abstract: Autonomous machine navigation techniques include using simulation to configure camera capture parameters. A method may include capturing image data of a scene, generating irradiance image data, determining at least one test camera capture parameter, determining a simulated scene parameter, and generating at least one updated camera capture parameter. Image data for camera capture configuration may be captured while the autonomous machine is moving. Camera captures parameters may be used to capture images while the autonomous machine is slowed or stopped, particularly in lowlight conditions.

Abstract: Autonomous machine (100) navigation techniques include using simulation to configure camera (133) capture parameters. A method may include capturing image data of a scene, generating irradiance image data, determining at least one test camera capture parameter, determining a simulated scene parameter, and generating at least one updated camera capture parameter. Image data for camera capture configuration may be captured while the autonomous machine is moving. Camera (133) captures parameters may be used to capture images while the autonomous machine (100) is slowed or stopped, particularly in lowlight conditions.

Abstract: Autonomous machine (100) navigation techniques include using simulation to configure camera (133) capture parameters. A method may include capturing image data of a scene, generating irradiance image data, determining at least one test camera capture parameter, determining a simulated scene parameter, and generating at least one updated camera capture parameter. Image data for camera capture configuration may be captured while the autonomous machine is moving. Camera (133) captures parameters may be used to capture images while the autonomous machine (100) is slowed or stopped, particularly in lowlight conditions.

Abstract: Vision systems for autonomous machines and methods of using same during machine localization are provided. Exemplary systems and methods may reduce computing resources needed to perform vision-based localization by selecting the most appropriate camera from two or more cameras, and optionally selecting only a portion of the selected camera's field of view, from which to perform vision-based location correction. Other embodiments may provide camera lens coverings that maintain optical clarity while operating within debris-filled environments.

Abstract: A method for obtaining a three-dimensional model of an inspection site, using a perception module, is disclosed. The perception module comprises a detection unit, e.g. comprising one or more cameras and/or a three-dimensional laser scanner, configured to obtain a three-dimensional image. At least one three-dimensional image is obtained by means of the detection unit. A three-dimensional model of surroundings of the perception module is created, based on the obtained three-dimensional image. The created three-dimensional model and a plan of the inspection site are compared and features of the created three-dimensional model and features of the plan of the inspection site are matched. A site-specific three-dimensional model of the inspection site is formed, based on the created three-dimensional model and the plan of the inspection site, and based on the comparison.

Abstract: Autonomous machine navigation techniques may generate a three-dimensional point cloud that represents at least a work region based on feature data and matching data. Pose data associated with points of the three-dimensional point cloud may be generated that represents poses of an autonomous machine. A boundary may be determined using the pose data for subsequent navigation of the autonomous machine in the work region. Non-vision-based sensor data may be used to determine a pose. The pose may be updated based on the vision-based pose data. The autonomous machine may be navigated within the boundary of the work region based on the updated pose. The three-dimensional point cloud may be generated based on data captured during a touring phase. Boundaries may be generated based on data captured during a mapping phase.

Abstract: A method for obtaining a three-dimensional model of an inspection site, using a perception module, is disclosed. The perception module comprises a detection unit, e.g. comprising one or more cameras and/or a three-dimensional laser scanner, configured to obtain a three-dimensional image. At least one three-dimensional image is obtained by means of the detection unit. A three-dimensional model of surroundings of the perception module is created, based on the obtained three-dimensional image. The created three-dimensional model and a plan of the inspection site are compared and features of the created three-dimensional model and features of the plan of the inspection site are matched. A site-specific three-dimensional model of the inspection site is formed, based on the created three-dimensional model and the plan of the inspection site, and based on the comparison.

Abstract: Autonomous machine (100) navigation techniques include using simulation to configure camera (133) capture parameters. A method may include capturing image data of a scene, generating irradiance image data, determining at least one test camera capture parameter, determining a simulated scene parameter, and generating at least one updated camera capture parameter. Image data for camera capture configuration may be captured while the autonomous machine is moving. Camera (133) captures parameters may be used to capture images while the autonomous machine (100) is slowed or stopped, particularly in lowlight conditions.

Abstract: Autonomous machine navigation techniques may generate a three-dimensional point cloud that represents at least a work region based on feature data and matching data. Pose data associated with points of the three-dimensional point cloud may be generated that represents poses of an autonomous machine. A boundary may be determined using the pose data for subsequent navigation of the autonomous machine in the work region. Non-vision-based sensor data may be used to determine a pose. The pose may be updated based on the vision-based pose data. The autonomous machine may be navigated within the boundary of the work region based on the updated pose. The three-dimensional point cloud may be generated based on data captured during a touring phase. Boundaries may be generated based on data captured during a mapping phase.

Abstract: Autonomous machine navigation techniques may generate a three-dimensional point cloud that represents at least a work region based on feature data and matching data. Pose data associated with points of the three-dimensional point cloud may be generated that represents poses of an autonomous machine. A boundary may be determined using the pose data for subsequent navigation of the autonomous machine in the work region. Non-vision-based sensor data may be used to determine a pose. The pose may be updated based on the vision-based pose data. The autonomous machine may be navigated within the boundary of the work region based on the updated pose. The three-dimensional point cloud may be generated based on data captured during a touring phase. Boundaries may be generated based on data captured during a mapping phase.