The application provides a pose adjustment method, a pose adjustment device, an electronic equipment, and a readable storage medium. The method includes: obtaining coordinates and noise parameters of the mobile device at a previous moment, the coordinates and the noise parameters at the previous moment being predicted by a Kalman filter model, the previous moment is a previous moment adjacent to a current moment; obtaining several measurement coordinates of the mobile device at the current moment through several measurement modules; predicting coordinates of the mobile device at the current moment through the Kalman filter model based on the several measured coordinates, the coordinates of the previous moment, and the noise parameters of the previous moment; and adjusting the pose of the mobile device at the current moment based on the coordinates of the current moment and a preset trajectory of the mobile device. The pose adjustment method may improve positioning accuracy.

Provided are an obstacle avoidance method for a self-propelled device, a medium, and a self-propelled device. The method includes acquiring a suspension height of an obstacle on a current travel route during traveling; determining whether the suspension height of the obstacle is within a preset limited height range, wherein the preset limited height range enables a part of the self-propelled device to pass the suspension height and an other part of the self-propelled device is limited by the suspension height; and acquiring, in response to determining that the suspension height of the obstacle is within the preset limited height range, current traveling state information of the self-propelled device, and determining whether to adjust the current travel route based on the current traveling state information to avoid the obstacle.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.