Abstract: Methods and systems for a materials handling vehicle comprising a processor and a sensor to record warehouse features. The processor is configured to generate and extract features from sensor data, create a factor graph (FG) including a sensor extrinsics node (e0), and generate an initial vehicle node (v0), initial sensor frame node (c0), and initial sensor feature node (f0) that comprises a selected extracted feature associated with c0 and v0 in an initial data association. A subsequent vehicle node (v1) is generated based on an accumulated odometry amount, and a subsequent sensor frame node (c1) is generated and associated with e0, v1, and one of f0 or a subsequent sensor feature node (f1) in a subsequent data association. The FG is optimized to provide a sensor calibration output based on the data associations, and the vehicle is navigated based on the sensor calibration output.

Abstract: Systems and methods for calibrating odometry of a materials handling vehicle. One embodiment of a method includes determining a current location of the materials handling vehicle, determining an odometry distance from the current location to a destination based on a calculation of a determined number of rotations of a wheel and a circumference of the wheel, and determining a positioning system distance from the current location to the destination. Some embodiments include comparing the odometry distance with data from the positioning system distance to calculate a scaling factor, applying the scaling factor to a fast alpha filter to achieve a fast filter result, and applying the scaling factor to a slow alpha filter to achieve a slow filter result. Similarly, some embodiments include applying the fast alpha filter to the scaling factor to smooth noise, calculating an updated odometry distance utilizing the scaling factor, and utilizing the updated odometry distance.

Abstract: Methods and systems for a materials handling vehicle comprising a processor and a sensor to record warehouse features. The processor is configured to generate and extract features from sensor data, create a factor graph (FG) including a sensor extrinsics node (e0), and generate an initial vehicle node (v0), initial sensor frame node (c0), and initial sensor feature node (f0) that comprises a selected extracted feature associated with c0 and v0 in an initial data association. A subsequent vehicle node (v1) is generated based on an accumulated odometry amount, and a subsequent sensor frame node (c1) is generated and associated with e0, v1, and one of f0 or a subsequent sensor feature node (f1) in a subsequent data association. The FG is optimized to provide a sensor calibration output based on the data associations, and the vehicle is navigated based on the sensor calibration output.

Abstract: Systems and methods for calibrating odometry of a materials handling vehicle. One embodiment of a method includes determining a current location of the materials handling vehicle, determining an odometry distance from the current location to a destination based on a calculation of a determined number of rotations of a wheel and a circumference of the wheel, and determining a positioning system distance from the current location to the destination. Some embodiments include comparing the odometry distance with data from the positioning system distance to calculate a scaling factor, applying the scaling factor to a fast alpha filter to achieve a fast filter result, and applying the scaling factor to a slow alpha filter to achieve a slow filter result. Similarly, some embodiments include applying the fast alpha filter to the scaling factor to smooth noise, calculating an updated odometry distance utilizing the scaling factor, and utilizing the updated odometry distance.

Abstract: Systems and methods for calibrating odometry of a materials handling vehicle. One embodiment of a method includes determining a current location of the materials handling vehicle, determining an odometry distance from the current location to a destination based on a calculation of a determined number of rotations of a wheel and a circumference of the wheel, and determining a positioning system distance from the current location to the destination. Some embodiments include comparing the odometry distance with data from the positioning system distance to calculate a scaling factor, applying the scaling factor to a fast alpha filter to achieve a fast filter result, and applying the scaling factor to a slow alpha filter to achieve a slow filter result. Similarly, some embodiments include applying the fast alpha filter to the scaling factor to smooth noise, calculating an updated odometry distance utilizing the scaling factor, and utilizing the updated odometry distance.

Abstract: Systems and methods for calibrating an image capture device for a materials handling vehicle. One embodiment of a system includes the image capture device and a vehicle computing device, where the vehicle computing device stores logic that when executed by a processor, causes the materials handling vehicle to determine a current location of the materials handling vehicle in a warehouse and determine a seed value associated with the image capture device, the seed value representing initial calibration parameters of the image capture device. In some embodiments, the logic causes the materials handling vehicle to capture image data in the warehouse, compare the image data with a site map, and determine a calibrated value for the image capture device from the comparison and the seed value.

Abstract: Systems and methods for calibrating odometry of a materials handling vehicle. One embodiment of a method includes determining a current location of the materials handling vehicle, determining an odometry distance from the current location to a destination based on a calculation of a determined number of rotations of a wheel and a circumference of the wheel, and determining a positioning system distance from the current location to the destination. Some embodiments include comparing the odometry distance with data from the positioning system distance to calculate a scaling factor, applying the scaling factor to a fast alpha filter to achieve a fast filter result, and applying the scaling factor to a slow alpha filter to achieve a slow filter result. Similarly, some embodiments include applying the fast alpha filter to the scaling factor to smooth noise, calculating an updated odometry distance utilizing the scaling factor, and utilizing the updated odometry distance.

Abstract: Systems and methods for calibrating odometry of a materials handling vehicle. One embodiment of a method includes determining a current location of the materials handling vehicle, determining an odometry distance from the current location to a destination based on a calculation of a determined number of rotations of a wheel and a circumference of the wheel, and determining a positioning system distance from the current location to the destination. Some embodiments include comparing the odometry distance with data from the positioning system distance to calculate a scaling factor, applying the scaling factor to a fast alpha filter to achieve a fast filter result, and applying the scaling factor to a slow alpha filter to achieve a slow filter result. Similarly, some embodiments include applying the fast alpha filter to the scaling factor to smooth noise, calculating an updated odometry distance utilizing the scaling factor, and utilizing the updated odometry distance.

Abstract: Systems and methods for calibrating an image capture device for a materials handling vehicle. One embodiment of a system includes the image capture device and a vehicle computing device, where the vehicle computing device stores logic that when executed by a processor, causes the materials handling vehicle to determine a current location of the materials handling vehicle in a warehouse and determine a seed value associated with the image capture device, the seed value representing initial calibration parameters of the image capture device. In some embodiments, the logic causes the materials handling vehicle to capture image data in the warehouse, compare the image data with a site map, and determine a calibrated value for the image capture device from the comparison and the seed value.

Abstract: Systems and methods for calibrating odometry of a materials handling vehicle. One embodiment of a method includes determining a current location of the materials handling vehicle, determining an odometry distance from the current location to a destination based on a calculation of a determined number of rotations of a wheel and a circumference of the wheel, and determining a positioning system distance from the current location to the destination. Some embodiments include comparing the odometry distance with data from the positioning system distance to calculate a scaling factor, applying the scaling factor to a fast alpha filter to achieve a fast filter result, and applying the scaling factor to a slow alpha filter to achieve a slow filter result. Similarly, some embodiments include applying the fast alpha filter to the scaling factor to smooth noise, calculating an updated odometry distance utilizing the scaling factor, and utilizing the updated odometry distance.

Abstract: According to the embodiments described herein, an industrial vehicle can include an Environmental Based Localization (EBL) sensor communicatively coupled to one or more processors. The EBL sensor can detect objects within a field of view. The one or more processors execute machine readable instructions to access a feature set and an occlusion set that are associated with an industrial facility. An occlusion path that intersects a detectable occlusion of the occlusion set and the sensor origin of the EBL sensor can be determined. A feature intersection of the occlusion path can be determined. A detectable feature can be classified as an occluded detectable feature based at least in part upon location of the feature intersection. The industrial vehicle can be navigated through the industrial facility utilizing the feature set exclusive of the occluded detectable feature.

Abstract: According to the embodiments described herein, an industrial vehicle can include an Environmental Based Localization (EBL) sensor communicatively coupled to one or more processors. The EBL sensor can detect objects within a field of view. The one or more processors execute machine readable instructions to access a feature set and an occlusion set that are associated with an industrial facility. An occlusion path that intersects a detectable occlusion of the occlusion set and the sensor origin of the EBL sensor can be determined. A feature intersection of the occlusion path can be determined. A detectable feature can be classified as an occluded detectable feature based at least in part upon location of the feature intersection. The industrial vehicle can be navigated through the industrial facility utilizing the feature set exclusive of the occluded detectable feature.