Abstract: A method and device for in-situ enriching Anammox bacteria by conventional activated sludge, which belongs to a biological wastewater treatment technology. The device comprises a raw water tank, an anammox reactor and a water outlet tank.

Abstract: The present disclosure provides a vehicle positioning method, an apparatus and an autonomous driving vehicle, relating to autonomous driving in the technical field of artificial intelligence, which can be applied to high-definition positioning of the autonomous driving vehicle, the method including: if there is no high-definition map in a vehicle, acquiring intermediate pose information of the vehicle based on a global navigation satellite system and/or an inertial measurement unit in the vehicle, and determining the intermediate pose information as global positioning information; acquiring local positioning information; performing fusion processing to the global pose information and the local pose information to obtain fused pose information; performing compensation processing to the fused pose information according to the global attitude angle information and the local attitude angle information to obtain a position of the vehicle.

Abstract: The present disclosure provides a method, an apparatus, a computer device and a computer-readable storage medium for positioning, and relates to the field of autonomous driving. The method obtains point cloud data collected by a LiDAR on a device at a current time; determines, based on the point cloud data and a global map built in a global coordinate system, global positioning information of the device in the global coordinate system at the current time; and determine, based on the point cloud data and a local map built in a local coordinate system, local positioning information of the device in the local coordinate system at the current time. A positioning result of the device at the current time is determined based on at least the global positioning information and the local positioning information. Techniques of the present disclosure can provide an effective and stable positioning service.

Abstract: The present disclosure provides a data processing method, an apparatus, an electronic device and a medium, which relates to the technical fields of autonomous driving, electronic maps, deep learning, image processing, and the like. The method includes: a computing device inputs a reference image and a captured image into a feature extraction model; obtain, a set of reference descriptors based on the first descriptor map; determine a plurality of sets of training descriptors; obtain a predicted pose of the vehicle by inputting the plurality of training poses and a plurality of similarities into a pose prediction model; and train the feature extraction model and the pose prediction model. When applied to a vehicle localization system, the trained feature extraction model and pose prediction model according to some embodiments of the present disclosure can improve accuracy and robustness of vehicle localization, thereby boosting the performance of the vehicle localization system.

Abstract: The present disclosure provides a method, an apparatus, a computer device and a computer-readable storage medium for positioning, and relates to the field of autonomous driving. The method obtains inertial measurement data of a device to be positioned at a current time and point cloud data collected by a LiDAR on the device at the current time; determines, by integrating the inertial measurement data, inertial positioning information of the device in an inertial coordinate system at the current time; and determines, based on the inertial positioning information, the point cloud data and at least one local map built in a local coordinate system, a positioning result of the device in the local coordinate system at the current time. Techniques of the present disclosure can provide an effective and stable local positioning result.

Abstract: A positioning method based on a lane line and a feature point, an electronic device, and a storage medium, which relate to fields of computer, automatic driving, intelligent transportation, computer vision. The method may include: determining first real-time measurement residuals according to first sensor information of a movable object detected, the first real-time measurement residuals including a first inertial measurement unit measurement residual, a first lane line measurement residual and a first non-lane line measurement residual; updating a state vector of the movable object according to a kinematic model of an inertial measurement unit and the first real-time measurement residuals; and determining a pose of the movable object at a time instant corresponding to the updated state vector according to a pose vector in the updated state vector.

Abstract: In one embodiment, a method for extracting point cloud features for use in localizing an autonomous driving vehicle (ADV) includes selecting a first set of keypoints from an online point cloud, the online point cloud generated by a LiDAR device on the ADV for a predicted pose of the ADV; and extracting a first set of feature descriptors from the first set of keypoints using a feature learning neural network running on the ADV, The method further includes locating a second set of keypoints on a pre-built point cloud map, each keypoint of the second set of keypoints corresponding to a keypoint of the first set of keypoint; extracting a second set of feature descriptors from the pre-built point cloud map; and estimating a position and orientation of the ADV based on the first set of feature descriptors, the second set of feature descriptors, and a predicted pose of the ADV.

Abstract: In one embodiment, a method for solution inference using neural networks in LiDAR localization includes constructing a cost volume in a solution space for a predicted pose of an autonomous driving vehicle (ADV), the cost volume including a number of sub volumes, each sub volume representing a matching cost between a keypoint from an online point cloud and a corresponding keypoint on a pre-built point cloud map. The method further includes regularizing the cost volume using convention neural networks (CNNs) to refine the matching costs; and inferring, from the regularized cost volume, an optimal offset of the predicted pose. The optimal offset can be used to determine a location of the ADV.

Abstract: The present invention discloses a shaft-tube joint structure of a carbon fiber reinforced plastic (CFRP) drive shaft. The shaft-tube joint structure includes a hollow shaft tube, a plurality of first rectangular teeth being uniformly and circumferentially arranged at both ends of the hollow shaft tube; two shaft-tube joints which are respectively fixed at two ends of the hollow shaft tube, the thickness of the shaft-tube joint being smaller than that of the hollow shaft tube, and an inner wall of the shaft-tube joint being smoothly connected to an inner wall of the hollow shaft tube; and a universal joint, an end thereof being tubular, a plurality of second rectangular teeth being uniformly and circumferentially arranged, the universal joint being matched with and sleeving the outer wall of the shaft-tube joint, and the second rectangular teeth being meshed with the first rectangular teeth.

Abstract: The present disclosure provides a method, an apparatus, a computing device and a computer readable storage medium for detecting an environmental change, and relates to the field of autonomous driving. The method obtains a global map for an area and a first local map built in real time for a sub-area in the area; and determines an environmental change in the first sub-area by comparing the first local map and the global map and determining a first probability of the environmental change. Techniques of the present disclosure can automatically detect environmental changes that affect the positioning of autonomous driving, thereby facilitating the updating of positioning maps.

Abstract: A method for calculating an arm angle range of a robot arm includes: determining a pose at an tail end position of the robot arm; judging whether an angle of an elbow joint is within a limit thereof when an arm angle is 180 degrees; if yes, constructing a first position matrix characterizing the elbow joint by using the pose and the arm angle; constructing a second position matrix of the elbow joint by using DH parameters of other joints of the robot arm; calculating, according to the first position matrix and the second position matrix, a first arm angle feasible region satisfying a limit of a position joint; judging whether the robot arm has a secondary position joint; and intersecting with the calculated first arm angle feasible region to obtain an arm angle range if no secondary position joint is present at the robot arm.

Abstract: The present disclosure provides a vehicle positioning method, an apparatus and an autonomous driving vehicle, relating to autonomous driving in the technical field of artificial intelligence, which can be applied to high-definition positioning of the autonomous driving vehicle, the method including: if there is no high-definition map in a vehicle, acquiring intermediate pose information of the vehicle based on a global navigation satellite system and/or an inertial measurement unit in the vehicle, and determining the intermediate pose information as global positioning information; acquiring local positioning information; performing fusion processing to the global pose information and the local pose information to obtain fused pose information; performing compensation processing to the fused pose information according to the global attitude angle information and the local attitude angle information to obtain a position of the vehicle.

Abstract: The disclosure provides a method, an apparatus, a device and a storage medium for positioning an object. The method includes: obtaining a map related to a region where the object is located, the map including a plurality of map layers having different height information; determining, based on the map and current point cloud data related to the object, an estimated position of the object, an estimated height corresponding to the estimated position and an estimated probability that the object is located at the estimated position with an estimated posture; and determining, at least based on the estimated position, the estimated height and the estimated probability, positioning information for the object, the positioning information indicating at least one of a current position of the object, a current height of the object and a current posture of the object.

Abstract: In one embodiment, a method for temporal smoothness in localization results for an autonomous driving vehicle includes: creating a probability offset volume that represents an overall matching cost between a first set of keypoints from the online point cloud and a second set of keypoints from a pre-built point cloud map for each of a series of sequential light detection and ranging (LiDAR) frames in an online point cloud. The method also includes compressing the probability offset volume into multiple probability vectors across a X dimension, a Y dimension and a yaw dimension; providing each probability vector of the probability offset volume to a number of recurrent neural networks (RNNs); and generating, by the RNNs, a trajectory of location results across the plurality of sequential LiDAR frames.

Abstract: The present disclosure provides a method, an apparatus, an electronic device and a storage medium for vehicle localization, which relates to the technical fields of autonomous driving, electronic map, deep learning, image processing, and the like. In the method, a computing device obtains an image descriptor map corresponding to a captured image of an external environment of a vehicle and a predicted pose of the vehicle when the captured image is captured; obtains a set of reference descriptors and a set of spatial coordinates corresponding to a set of keypoints of a reference image of the external environment; determines a plurality of sets of image descriptors corresponding to the set of spatial coordinates when the vehicle is in a plurality of candidate poses, respectively; determines a plurality of similarities between the plurality of sets of image descriptors and the set of reference descriptors; and updates the predicted pose based on the plurality of candidate poses and the plurality of similarities.

Abstract: The present disclosure provides a data processing method, an apparatus, an electronic device and a medium, which relates to the technical fields of autonomous driving, electronic maps, deep learning, image processing, and the like. The method includes: a computing device inputs a reference image and a captured image into a feature extraction model; obtain, a set of reference descriptors based on the first descriptor map; determine a plurality of sets of training descriptors; obtain a predicted pose of the vehicle by inputting the plurality of training poses and a plurality of similarities into a pose prediction model; and train the feature extraction model and the pose prediction model. When applied to a vehicle localization system, the trained feature extraction model and pose prediction model according to some embodiments of the present disclosure can improve accuracy and robustness of vehicle localization, thereby boosting the performance of the vehicle localization system.

Abstract: A motion planning method for robot arms includes: calculating an arm angle range of each pose of a tail end of a robot arm in the motion trajectory; calculating a start arm angle value of a start pose of the tail end of the robot arm in the motion trajectory; calculating an arm angle proportion according to the start arm angle value and the arm angle range of the start pose; identifying an abruptly changing arm angle range in the arm angle range of each pose according to a upper boundary curve and a lower boundary curve; calculating an arm angle of a pose corresponding to the abruptly changing arm angle range; calculating arm angles corresponding to the other poses according to the arm angle proportion; and calculating an angle of a joint of the robot arm.

Abstract: This application relates to the field of visual navigation technology, and discloses a localization method, apparatus, robot and computer storage medium. The localization method is applied to a robot having an autonomous location and navigation function. The localization method includes: determining that localization through an image of surrounding environment fails in a process of driving to a preset distance, and controlling the robot to decelerate and perform localization through an image of surrounding environment in a process of deceleration until the localization succeeds. The localization method enables the robot to reasonably adjust its running velocity in accordance with the environment during driving, thereby accurately localizing the location where the robot is located.