A method of tracking an object using a LiDAR sensor includes: acquiring a point cloud using each of LiDAR sensors and clustering the point cloud. The clustering includes generating a three-dimensional (3D) grid map using the point cloud acquired by each of the LiDAR sensors; labeling voxels present in the 3D grid map with one cluster using direction points of the voxels; and checking whether it is necessary to label the voxels with mutually different clusters.

A system for detecting and tracking objects using lidar can include one or more processors configured to receive lidar data. The one or more processors can determine shape data from the lidar data. The shape data can be indicative of an object. The one or more processors can determine a plurality of extents of the object based on the shape data. The one or more processors can update a state of the object based on the plurality of extents, the state including a boundary of the object. The one or more processors can provide the state of the object to an autonomous vehicle controller to cause the autonomous vehicle controller to control an autonomous vehicle responsive to the state of the object.

The disclosure relates to a method and system for determining a moving/stationary state of a tracking target based on a neural network. The method includes: a training step of extracting feature points for each tracking target in a tracking target set, converting the feature points into feature vectors and labeling the feature vectors with truth values of moving and stationary states of the tracking target, and training a neural network by using the feature vectors and the corresponding truth values of the moving and stationary states as training sample parameters, to obtain a trained neural network classification model; and a testing step of extracting feature vectors of the tracking target, and then inputting the feature vectors into the neural network classification model, to obtain a moving and stationary state classification result for the tracking target. According to the disclosure, the use of the neural network method for determination of a moving/stationary state of the tracking target from the laser radar can improve the accuracy of a determination result.

The disclosure relates to a method and system for determining a moving/stationary state of a tracking target based on a neural network. The method includes: a training step of extracting feature points for each tracking target in a tracking target set, converting the feature points into feature vectors and labeling the feature vectors with truth values of moving and stationary states of the tracking target, and training a neural network by using the feature vectors and the corresponding truth values of the moving and stationary states as training sample parameters, to obtain a trained neural network classification model; and a testing step of extracting feature vectors of the tracking target, and then inputting the feature vectors into the neural network classification model, to obtain a moving and stationary state classification result for the tracking target. According to the disclosure, the use of the neural network method for determination of a moving/stationary state of the tracking target from the laser radar can improve the accuracy of a determination result.

A target recognition device includes a target recognition unit, a tracking unit, a target registration unit, a condition determination unit, and a registration requirement setting unit. The condition determination unit determines whether a target satisfies predetermined conditions J1 to J3. The registration requirement setting unit sets a requirement for registering a target by the target registration unit to be stricter for the target determined to satisfy all of the conditions J1 to J3 than for the target determined to satisfy not all of the conditions J1 to J3.

A light detection and ranging (LIDAR) system performs a method including generating a frequency domain waveform based on a baseband electrical signal in a time domain, wherein the frequency domain waveform includes a spectrum of frequencies and determining a likelihood metric for the spectrum of frequencies of the frequency domain waveform. The method further includes in response to one or more parameters associated with the frequency domain waveform satisfying a condition, modifying the likelihood metric for the spectrum of frequencies based on the one or more parameters associated with the frequency domain waveform to generate a modified likelihood metric for the spectrum of frequencies, selecting a peak frequency from the frequency domain waveform corresponding to a frequency with the highest value for the modified likelihood metric, and determining one or more properties of a target based at least in part on the selected peak frequency.

The present invention relates to a surveying apparatus for surveying an object as well as a surveying system comprising the surveying apparatus having a simple and compact optical setup. The surveying apparatus comprises a lens arrangement including at least one movably arranged focus lens element for focusing to sight an object; an imaging unit configured to obtain an image of at least a part of the object; a distance measuring unit configured to measure a distance to the object along the optical axis of the distance measuring unit; and a beam splitter/combiner configured to combine a part of the optical imaging path of the imaging unit and a part of the optical distance measuring path of the distance measuring unit so that the optical axis of the imaging unit and the optical axis of the distance measuring unit are at least coaxially arranged with the optical axis of the lens arrangement between the lens arrangement and the beam splitter/combiner.

The present invention relates to a surveying apparatus for surveying an object as well as a surveying system comprising the surveying apparatus having a simple and compact optical setup. The surveying apparatus comprises a lens arrangement including at least one movably arranged focus lens element for focusing to sight an object; an imaging unit configured to obtain an image of at least a part of the object; a distance measuring unit configured to measure a distance to the object along the optical axis of the distance measuring unit; and a beam splitter/combiner configured to combine a part of the optical imaging path of the imaging unit and a part of the optical distance measuring path of the distance measuring unit so that the optical axis of the imaging unit and the optical axis of the distance measuring unit are at least coaxially arranged with the optical axis of the lens arrangement between the lens arrangement and the beam splitter/combiner.

The invention relates to a method of tracking a target on an orbital trajectory by a spacecraft, the method comprising an acquisition phase which comprises the steps of activating a lidar, acquiring signals from the lidar system, determining target trajectory data from the lidar signals, wherein the spacecraft is engaged on a trajectory to approach or inspect the target, which trajectory is determined based on the target trajectory data, and if the target is no longer detected, activating a short-range detection phase, comprising activation of a wide-field radar.

A method for determining situational awareness in a worksite includes setting at least one environment modelling apparatus (EM) at least one of: on a machine or external from the machine; setting at least one tracking apparatus (TA) at least one of: on the machine or external from the machine; acquiring data by the at least one tracking apparatus (TA); and acquiring data by the at least one environment modelling apparatus. Further, the method includes receiving, by at least one position determination unit (PDU), data related to the at least one tracking apparatus (TA) and data related to the at least one environment modelling apparatus (EM); and determining, by the at least one position determination unit (PDU), based at least in part on the received data, the location and orientation of the machine in the worksite.