6 results about "Multidimensional space" patented technology

There is provided a method, device and computer program for navigating an agent in a multidimensional space, the method comprising: generating a data field that spatially corresponds to a multidimensional space in which an agent resides, wherein the data field is populated with observation comparison measure data with respect to a stored observation, the stored observation including second sensor data describing an environment of a first position in the multidimensional space, wherein the generating the data field comprises: obtaining a plurality of current observations made by the agent at different respective positions in the multidimensional space, the current observations including first sensor data describing an environment of each of the different respective positions in the multidimensional space; comparing each of the current observations with the stored observation to obtain respective observation comparison measure data for the different respective positions in the multidimensional space; and populating the data field with the observation comparison measure data according to the different respective positions in the multidimensional space; the method further comprising: determining a reliability measure associated with the stored observation by performing a reliability check of the data field; and managing a digital map of the multidimensional space by maintaining, updating, or deleting information in the digital map regarding the stored observation based on the reliability measure.

A method and system for explainable classification of a target point cloud may include receiving a target point cloud comprising N points in a multidimensional space, applying a feature extraction model to extract, for each point, a permutation-invariant feature vector comprising local feature entries, applying a bottleneck function on the feature vectors to produce a global feature vector having F global feature entries, applying a classification model on the global feature vector to classify the target point cloud according to classification criteria, for one or more points, applying an aggregation function over the local feature entries of the respective feature vector, prior to the bottleneck function, to obtain an aggregation value, and indicating importance of the points in the classification based on their aggregation values.

This invention discloses an online uniformity monitoring method and system for TMR mixing process based on dynamic particle morphology recognition, relating to the field of intelligent monitoring technology in feed processing. The method includes: during material mixing, real-time acquisition of dynamic images of the material within the mixing chamber; identification of material particles in the dynamic images; extraction of the characteristic length of each particle; and calculation of skewness and kurtosis based on the characteristic length; division of the effective observation area of ​​the dynamic images into multiple sub-regions based on the centroid coordinates of all material particles in the dynamic images; extraction of particle characteristic statistics within each sub-region; and construction of a multidimensional spatial uniformity index set to characterize the mixing state of the material; input of the skewness, kurtosis, and multidimensional spatial uniformity index set into a pre-trained evaluation model, outputting a comprehensive uniformity index and corresponding quality level.

A method for rapidly detecting a straight line, a plane and a hyperplane in a multi-dimensional space. The new method has two important advantages: firstly, the model corresponds to a total least square fitting algorithm and has better tolerance to data noise, so as to solve the problem of the precision of detecting a target on a parameter space segmentation line by means of fast Hough transform being too low; and secondly, in the integrated fast Hough transform, targets that are close to each other in a data space are gathered together in a parameter space, and a calculation process in the parameter space can be displayed by using a visual graph, thereby rapidly determining the number of targets, guiding the setting of system parameters, and distinguishing a target that is repeatedly recognized.

The present disclosure relates to a vehicle radar system (102) for automatic multi-clutter removal in multidimensional space and a method (400) for this purpose. The system (102) includes a controller (108) that removes one or more ground points from a multidimensional LiDAR (Light Detection and Ranging) space containing point cloud data (PCD) using a LiDAR ground point removal technique. The controller (108) processes one or more radar signals to project radar PCD onto a dense multidimensional space containing a LiDAR voxel grid and to classify the voxel status of an object. The controller (108) dynamically predicts and identifies one or more superimposed radar PCDs that lie on the dense LiDAR voxel grid.The controller (108) filters out the multi-clutter with the binary condition “0” and retains the PCD data with the binary condition “1” to automatically generate ground truth data to train a deep learning model that initiates automatic multi-clutter removal based on ground truth data in the vehicle radar system (102).

The application discloses a mechanical arm path planning method based on an improved fast extended random tree and belongs to the technical field of industrial robot control. According to a set safety distance, a collision-free six-axis joint value sequence set from a starting point to an end point is quickly obtained. In initial path searching, a space preset tree strategy is used to guarantee the connection between an explored mechanical arm pose and initial and final mechanical arm poses and to make different poses as possible as to spread in space; in a path searching algorithm, a bidirectional search tree algorithm, a target bias, a greedy strategy and a variable step length strategy are used to accelerate algorithm convergence; a loop pruning algorithm is used in path optimization to greatly reduce a redundant path and to guarantee the local optimality of the path in an iteration process. The method improves the path planning efficiency of the mechanical arm in a three-dimensional space, solves the problems of long kinematics solving time of the mechanical arm in a multi-dimensional space and a redundant path and realizes a collision-free motion task in a joint space.