The invention provides a rotating shaft direction and rotating angular velocity resolving method for a moving rotating object, which comprises the following steps of: firstly, registering an adjacentpoint cloud at the moment by using a nearest point iteration point cloud registration algorithm, and constructing a local track matrix by using a registration matrix; secondly, point position information is obtained according to a local track matrix, point track fitting is carried out, the speed of a certain point at a certain moment is solved, and finally an equation is established according to the rigid body rotation physical process to carry out rotation parameter resolving; according to the method, dependence on feature points is avoided, and the motion condition of a target at a certain moment is described by using a local track matrix; good robustness is achieved on point cloud local loss caused by self-shielding of the target, and errors caused by incomplete feature point tracks inthe sequence point cloud are avoided; the method can effectively solve the rotating shaft and the rotating angular velocity, especially when the angular velocity of the rotating object is variable motion, the solving result can better track the change of the set angular velocity, the solving precision is higher, and the solving advantage is more obvious.

Method for calculating rotating shaft direction and rotating angular velocity of moving rotating object

The invention provides an inertia/radar based UAV autonomous positioning and mapping method, relates to the field of laser radar sensors and UAVs, and aims at solving problems in automatic positioningand environment sensing of a UAV without GPS information and establishing an accurate environment map in real time. The method comprises the following steps of 1) feature extraction, in which line and plane features are extracted from point cloud data; 2) radar mileage metering, in which point-to-line and point-to-plane distances between twice radar scanning serve as optimization indexes, featurematching is carried out in an L-M method to estimate pose information of the UAV, and present radar scanning data is distorted again; 3) establishment of the environment map, non-distorted point cloud data obtained in the last step accurately matches a map layer in the L-M method to obtain more accurate pose information of the UAV, and an accurate 3D map is established; and 4) loop-back detection. The method is mainly applied to UAV design and manufacture.

Inertia/radar based unmanned aerial vehicle (UAV) autonomous positioning and mapping method

A loopback detection method based on point cloud segment matching constraint and trajectory drift optimization is proposed, which adopts real-time lidar odometer and LOAM system, but the LOAM system will have large drift after long-time mapping, so a method based on point cloud segment matching constraint and trajectory drift optimization is proposed. By segmenting, describing and matching the point cloud acquired by lidar, the loop relationship is found, and the loop is adjusted by using explicit loop closure ELCH algorithm after the loop is found, and an optimization algorithm is proposed toeliminate the local inconsistencies in the adjustment process of point cloud map. After optimization, the invention proposes a posture prediction and compensation algorithm to predict and compensatethe posture after the loop. So as to maximize the effect of reducing drift error.

A loop detection method based on point cloud segment matching constraint and trajectory drift optimization

The invention provides a graph optimization method and device, electronic equipment and a storage medium. The method comprises the following steps: pre-constructing a to-be-optimized graph of SLAM (Simultaneous Localization and Mapping), wherein a node of the graph represents a to-be-optimized variant of a to-be-solved linear equation, an edge represents a constraint relation of the to-be-optimized variant, the node comprises a first node and a second node and respectively represents the three-dimensional point coordinate and the camera motion parameter; copying the first node in the to-be-optimized graph into more than two third nodes, wherein any one of the third nodes and n second constraint nodes have a constraint relation, m of the n second constraint nodes and the adjacent third nodehave the same constraint relation, the second constraint node and at least one third node have a constraint relation, n is a preset control parameter, and the second constraint node is the second node having the constraint relation with the first node; and marginalizing the third node to reduce the density of the to-be-solved linear equation. The method provided by the embodiment of the inventionimproves the optimization efficiency, and reduces the power consumption in computation and the hardware cost.

Graph optimization method and device, electronic equipment and storage medium

The invention discloses an SLAM technology-based spacecraft relative autonomous navigation system. The SLAM technology-based spacecraft relative autonomous navigation system comprises a synchronous positioning and mapping part, a target pose estimation part, a spacecraft autonomous navigation planning part and a ground simulation system. The SLAM technology-based spacecraft relative autonomous navigation system adopting a laser radar as a pose obtaining laser in a target-approaching service stage in a complicated space scene has the advantages of high precision and wide application range, andcan solve the relative position and attitude measurement problem of a service spacecraft and a non-cooperation target.

SLAM technology-based spacecraft relative autonomous navigation system

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SLAM technology-based spacecraft relative autonomous navigation system