177 results about "Visual navigation" patented technology

The invention relates to the technical field of unmanned aerial vehicle navigation, and discloses an unmanned aerial vehicle navigation method and system based on computer vision, and the unmanned aerial vehicle navigation method based on computer vision comprises the following steps: collecting a current visual field image of an airborne camera of an unmanned aerial vehicle, and carrying out adaptive image enhancement; performing scene semantic understanding; semantic perception feature extraction is carried out; feature matching and outer point elimination are carried out; floor identification is carried out, and multi-sensor fusion positioning is carried out; plane segmentation, structural constraint extraction and map normalization are carried out; performing visual navigation evaluation, judging a navigation mode according to an evaluation result, and generating an exploration control instruction quantity based on the navigation mode; generating a cross-floor path sequence, optimizing a track in real time, and performing control fusion in combination with a navigation mode identifier and an exploration control vector; according to the method, the navigation problem of the unmanned aerial vehicle in complex scenes such as smoke interference, multi-story buildings and illumination dramatic change in a GPS denial environment is solved, and high-precision autonomous navigation is realized.

The invention discloses a multi-agent collaborative visual navigation reasoning enhancement method and system, and the method comprises the steps: an inference agent analyzes input data, and generates structured output containing a reasoning process and an initial answer; independently analyzing the input data by the evaluator agent, generating an evaluation answer and reviewing the output of the inference agent; comparing whether the initial answer is consistent with the evaluation answer, and if yes, outputting a final answer; if not, calling a bifurcation feedback agent, and generating a core bifurcation point abstract and a visual evidence verification plan; and calling a final decision maker agent to directionally retrieve the video evidence according to the visual evidence verification plan, and outputting a final answer. Four roles of an inference person, an evaluator, a divergence feedback person and a final decision maker are introduced, a complete'generation-evaluation-feedback-optimization 'decision link is constructed, and the cognition and inference accuracy in a complex scene is remarkably improved.

The invention relates to the technical field of visual navigation, in particular to a blind person navigation path planning method combining visual SLAM and semantic segmentation. The method comprises the following steps: acquiring an environment image and pose data; performing front-end tracking by using the pose data, and determining a key frame sequence; generating a depth map based on the environment image, and identifying a passable area according to the depth map; constructing a three-dimensional point cloud map according to the key frame sequence; semantic segmentation is carried out according to the three-dimensional point cloud map, and obstacle type labels are recorded; according to the obstacle type label, carrying out safety level layering on the passable area, and determining a path cost weight; performing global path planning based on the path cost weight, and generating a semantic enhancement navigation trajectory; and driving real-time voice guidance by using the semantic enhancement navigation trajectory, and calculating a path execution deviation corresponding to the real-time voice guidance. According to the method, obstacle types are distinguished through semantic segmentation based on a visual navigation technology, a semantic enhancement path is generated, semantic hierarchical navigation is realized, and the intelligence of path decision is improved.

The invention discloses a multi-module collaborative navigation system for an intelligent robot, which relates to the field of robot navigation and comprises a multi-mode perception decision module, a semantic visual navigation module, a path memory fusion module and a rolling predictive control module which are electrically connected in sequence and work cooperatively. The multi-modal perception decision module performs weighted fusion on heterogeneous information, outputs scene representation and decision, and provides data support; the semantic visual navigation module analyzes the natural language instruction and combines visual data to complete path planning; the path memory fusion module carries out backtracking correction in an abnormal environment of semantic ambiguity and perception mismatch; and the rolling predictive control module realizes trajectory tracking and dynamic obstacle avoidance. According to the system, dependence on a prior map is reduced, instruction performability and dynamic obstacle avoidance capability are improved, collision risks are reduced, navigation real-time performance, stability and robustness are enhanced, and the system is compatible with multiple chassis and is suitable for complex dynamic scenes.

The invention discloses an unmanned aerial vehicle high-altitude cleaning path precise planning method and system based on visual navigation, and the method comprises the following steps: constructing an initial three-dimensional grid model of a to-be-cleaned building, dividing cleaning regions according to the initial three-dimensional grid model, and setting priorities; the unmanned aerial vehicle collects visual data of a cleaning area through a multi-mode visual sensor, wherein the visual data comprise surface texture, depth information and a stain distribution thermodynamic diagram; performing local correction on the initial three-dimensional grid model based on the real-time visual data to generate a high-precision dynamic three-dimensional grid model; generating a cleaning path by adopting an adaptive grid traversal algorithm according to the priority of the cleaning area and the coverage range of the unmanned aerial vehicle cleaning head; obstacles in the path are detected in real time through a visual feature matching algorithm, and when the obstacles are detected, path re-planning is carried out to avoid the obstacles until all the areas are cleaned. According to the method, the modeling precision and the dynamic adaptability are improved, and the intelligence and the pertinence of path planning are realized.

The invention provides an inertia / satellite / vision adaptive integrated navigation method and system. The method comprises the following steps: carrying out inertia measurement and navigation calculation to obtain inertia data; calculating satellite navigation observed quantity according to the satellite navigation data and the inertial data, and constructing a satellite observation model; calculating visual navigation observed quantity according to the visual navigation data and the inertial data, and constructing a visual observation model; aiming at a satellite and a visual observation model, respectively adopting an innovation-based adaptive covariance estimation method to obtain satellite and visual observation noise covariance updated values; constructing a satellite and visual navigation health degree, and calculating a satellite and visual fusion weight according to the satellite and visual navigation health degree; controlling updating of satellite and visual navigation observed quantity according to the satellite and visual fusion weight; and calculating a weighted equivalent observation matrix and a noise covariance, and carrying out filtering estimation. According to the method, an adaptive noise estimation and sensor health degree evaluation mechanism is introduced, dynamic weighted fusion of multi-sensor data is realized, and the robustness and precision of a navigation system in complex environments such as satellite signal lock losing and visual feature missing are improved.

The invention provides a multi-source remote sensing collaborative recognition method and system for road passing height-limiting obstacles, and relates to the technical field of road height-limiting recognition. Pixel-level elevation statistics is carried out by moving a sliding window in an elevation information block to position M height-limiting obstacles, and then clearance height calculation is carried out to correct and output M reference height-limiting heights to be projected to a passing road network; and according to the vehicle type of the freight vehicle and the real-time load information, height-limited negative compensation is carried out to output a dynamic passing height so as to traverse a passing road network to locate R feasible routes and display the R feasible routes to a vehicle-mounted display screen of the freight vehicle. The technical problems that in the prior art, due to the fact that manual inspection is relied on for height limiting obstacle state monitoring, the height limiting state is not updated in time, and due to the fact that a static height threshold value is adopted for vehicle height limiting passing judgment, large vehicle passing path planning is affected are solved. The technical effects of dynamically tracking the state change of the height-limiting obstacle and the passing height of the vehicle and carrying out precise path planning and visual navigation of the large vehicle are achieved.

The invention discloses a multi-machine collaborative three-dimensional map updating method based on mobile augmented reality, and belongs to the technical field of three-dimensional reconstruction, and the method comprises the following steps: carrying out the collection of initial collection data of a plurality of target regions through the wearing of AR glasses by a plurality of people; performing frame extraction on the original video image, and calculating to obtain a data grouping result; a sparse pairing list is generated by adopting a hybrid pairing strategy, and sparse point cloud data is obtained by combining incremental SfM calculation; performing distortion removal processing on the frame extraction image, and obtaining pure dense point cloud data through a multi-view solid geometry algorithm, a clustering algorithm and statistical filtering; a triangular mesh model is generated through Poisson surface reconstruction, and a high-precision three-dimensional model is obtained through Markov random field optimization view selection; and constructing a unified space-time reference conversion matrix to obtain a real-time AR visual navigation and map updating result. By adopting the method, the problems that the three-dimensional map is poor in current situation, virtual and real registration is misplaced, the modeling efficiency is low and the precision is insufficient are solved.

The invention discloses a dual-path non-cooperative spacecraft pose estimation method based on uncertainty perception, and belongs to the technical field of spacecraft pose estimation. According to the method, a dual-path prediction framework based on a shared backbone network is constructed and comprises a geometric reasoning path and a global context sensing path. In the training stage, prediction results of the two paths are aligned through a geometric consistency loss function, and joint optimization is carried out by combining multi-task losses such as key point regression, classification, rotation and translation regression and the like. In the inference stage, the pose estimation result is adaptively fused according to the predicted variance output by each path, when the variance is low, the output of the global context sensing path is directly adopted, otherwise, the key point projections of the two paths are combined, and the final pose is recovered. The method has higher estimation precision, robustness and interpretability in complex space environments such as shielding, illumination variation and large-scale variation, and provides reliable visual navigation support for on-orbit autonomous tasks.

The invention belongs to the technical field of mouse neuroscience research, and relates to a multi-modal mouse virtual reality and nerve recording integrated system based on skull anchoring, which comprises a skull anchoring basic support, a binocular miniature VR display module, a multi-modal fixed expansion frame and a behavior and power supply platform. According to the invention, the skull screw is used as a unique space reference, accurate limiting of the head is realized through rapid butt joint of the MMA and the external fixing rod, and rapid plugging and coordinate multiplexing of equipment for two-photon imaging, optical genetic stimulation and the like are realized in combination with an M2 anchoring hole preset in the MMA; the total mass of the whole machine is strictly controlled within 3.2 g; the optical system provides panoramic visual stimulation with a horizontal field angle of 140 degrees, completely covers a key binocular vision area on the top of the mouse head, and supports high-order cognitive task paradigms such as complex ecological visual navigation, spatial memory and social interaction.

The invention discloses an unmanned aerial vehicle visual navigation method based on infrared semantic feature matching and inter-frame anchoring, and aims at the core requirement of unmanned aerial vehicle autonomous navigation in a GNSS denial environment, the stability of infrared imaging and the robustness of high-level semantic features are fully combined, and the high-precision, continuous and stable visual autonomous navigation capability is realized. The method is especially suitable for complex scenes such as low-illumination, weak-texture and large-area homogenized areas at night. Through innovation in the aspects of infrared semantic map construction, deep semantic feature extraction, multi-scale semantic matching, semantic anchoring key frame selection, inter-frame recursion positioning and closed-loop correction, Bayesian fusion estimation longitude and latitude solution and the like, the positioning precision and robustness of the unmanned aerial vehicle in GNSS denial, low-visibility and large-area homogeneous scenes are remarkably improved.

The invention discloses a video target tracking system based on historical information dynamic propagation and future frame prediction collaborative optimization, which belongs to the field of visual target tracking and comprises a dynamic attention token module, a multi-constraint future prediction module, a real-time bidirectional calibration module, a tracking decision module and a scene adaptive memory module. The dynamic attention token module is used for receiving continuous video frames and generating a multi-scale token sequence dynamically adjusted along with target states, and the target states comprise a shielding area, a movement speed and an appearance scale; the target tracking robustness problem in a complex scene is solved by dynamically fusing the historical trajectory features of the target and future state prediction, and the method can be applied to the fields of intelligent monitoring, automatic driving visual perception, unmanned aerial vehicle inspection, robot visual navigation and the like. A bidirectional optimization mechanism based on historical token propagation and future frame prediction breaks through the limitations of insufficient utilization of time sequence information and insufficient dynamic adaptability in the prior art.

The invention belongs to the technical field of logistics management, particularly relates to a method for automatically stacking material cages in an unmanned forklift carriage, and is suitable for automatic loading and unloading scenes in the fields of logistics, manufacturing and the like. The core of the method is to solve the problem of high-precision and tight stacking of multiple columns and multiple rows of material cages in a long and narrow compartment space of the unmanned forklift. Through combination of 3D environment perception, laser / visual SLAM navigation, carriage wall fitting and tracking control based on point cloud and differential accurate positioning strategies for side cages and middle cages, unmanned and high-density stacking operation of the whole process is achieved. Wherein for the placement of the middle material cage, a visual auxiliary positioning and error compensation mechanism based on the placed material cage supporting legs is innovatively introduced, the track accumulative error is effectively overcome, and the alignment and compactness of the whole row of material cages are ensured. The automation level, the space utilization rate and the operation safety of loading and unloading operation are remarkably improved.

The invention provides a method, a device and equipment for identifying path deviation in a screw implanting process, and belongs to the field of data processing. The method comprises the steps that target stress information and pose information of the tail end of a target robot used for implanting a target screw are obtained, and the tail end of the target robot holds the target screw; determining a current implantation path of the target screw according to the target stress information and the pose information; obtaining a target implantation path, wherein the target implantation path is generated based on preoperative image data of a patient; and determining a path offset condition according to the current implantation path and the target implantation path, wherein the path offset condition is used for indicating whether path offset exists or not. The method and the device are used for recognizing path deviation in the nail implanting process based on a robot, and the path precision judgment capability and the operation safety of a robot operation system are improved. And meanwhile, additional visual navigation equipment is not needed, so that the system complexity and cost are reduced.

The invention discloses a multi-source fusion deep learning visual navigation method, system and device and a storage medium, and the method comprises the steps: collecting a visual image sequence and a Beidou positioning signal obtained by a robot in an operation process, carrying out the preprocessing, and generating a fusion input sequence with a quality score; performing two-channel feature coding on the visual image sequence and the Beidou positioning signal, mapping the features to a unified joint representation space, and performing dynamic modeling on the reliability of each mode in the current environment; based on the confidence coefficient of each mode and the cross-mode correlation, generating a double-branch dynamic mask of vision and Beidou, and combining a confidence coefficient modulation vector to fuse the two types of features to obtain a fused feature sequence; and performing context modeling on the fused feature sequence, constructing a joint optimization objective function for cooperative training, and outputting a robot navigation trajectory. According to the invention, the navigation precision, reliability and autonomy of the robot in a complex environment can be improved.

The application discloses a kind of based on visual navigation substation unmanned aerial vehicle inspection method and system, comprising: based on the video stream data of unmanned aerial vehicle acquisition, obtain multiple continuous video frame images;Video frame image is input into feature extraction network, and the substation road environment feature is extracted, and the extracted feature is respectively input into yaw angle prediction network and horizontal flight prediction network, and the yaw angle parameter of each direction substation road and the translation parameter required for unmanned aerial vehicle flight are output;According to the parameter information output, based on Gaussian mixture model, determine the yaw angle of unmanned aerial vehicle, and then generate unmanned aerial vehicle rotation direction control instruction and generate unmanned aerial vehicle translation control instruction;According to control instruction, control unmanned aerial vehicle flight, and carry out inspection.The application learns through the visual image of substation, so that unmanned aerial vehicle can recognize the pre-set inspection path and surrounding environmental obstacles, get rid of the dependence on GPS navigation signal, realize substation unmanned aerial vehicle automatic inspection.

Disclosed in the present invention is a navigation and positioning apparatus for maxillary sinus lift surgery, belonging to the technical field of medical devices. The technical problems in the prior art of increased surgical difficulty and elevated surgical risk caused by invisibility during the maxillary sinus lift procedure are solved. The present invention comprises a navigation and positioning device, a connecting shaft detachably connected to the navigation and positioning device, and a stripping head detachably connected to the connecting shaft for a maxillary sinus mucosa stripping operation. The present invention combines the maxillary sinus lift instrument with the navigation and positioning device recognizable by a visual navigation instrument, such that the functions of guidance and positioning, as well as positioning of the stripping depth and range, can be effectively achieved, thereby effectively reducing the difficulty and surgical risk of maxillary sinus lift surgery. Moreover, a detachable connection mode is used both between the stripping head and the connecting shaft, and between the connecting shaft and the navigation and positioning device, such that an operator can select a suitable elevator according to the specific condition of a patient, which can increase the application range and the convenience of use of the maxillary sinus lift instrument.

The application relates to an intelligent weeding device based on visual navigation and a weeding method, and relates to the technical field of intelligent weeding devices. In order to solve the problems in the prior art that the transverse movement of a weeding machine in a field and the increase and decrease of the number of weeding components need to be manually adjusted during use, the working process is easy to cause damage to the field ridge, the use convenience of the weeding machine is reduced, mechanical weeding is mostly inter-row weeding, the effect on inter-plant weeds is not ideal, weeds cannot be intelligently identified, the working path of the weeding machine cannot be planned for efficient weeding and the like, the application comprises a connecting frame, a translation mechanism, a turnover mechanism and a visual navigation mechanism; one side of the connecting frame is movably connected with a weeding mechanism, and the other side is connected with a supporting wheel; the weeding mechanism comprises a rack, weeding shovels and weeding fingers; the rack is movably connected to the connecting frame; the rear side of the rack is provided with a plurality of weeding shovels and a plurality of weeding fingers; and the upper end of the rack is provided with the visual navigation mechanism. The application is used for farmland weeding.

The utility model relates to the technical field of automatic recharging, in particular to an automatic recharging system adopting a robot visual navigation and infrared receiving mode, which comprises a robot main body and a charging seat, and the robot main body comprises a power supply module, an infrared receiving module, a camera, an electric quantity detection module, a main control module, a driving module and a load. The charging seat comprises a charging circuit and an infrared transmitting module, and the infrared receiving module and the infrared transmitting module are matched with each other. According to the utility model, the robot visual navigation and the infrared receiving are combined, the advantages of the robot visual navigation and the infrared receiving are brought into full play, in an environment with good light, the camera collects surrounding environment images, and after the images are analyzed by the image processing unit, accurate position information is provided for the robot, and the robot is guided to move towards the charging seat; when light is poor or blocked, the infrared receiving module is matched with the infrared transmitting module of the charging base, it is ensured that the robot can stably receive signals, the environmental adaptability of the system is effectively enhanced, and the recharging success rate is increased.

The present application belongs to the technical field of unmanned aerial vehicle navigation, and relates to a bamboo forest unmanned aerial vehicle full-autonomous visual navigation method and system fusing human operation experience. The system integrates multiple source perception units to construct a three-dimensional scene and a risk map; the pilot's line of sight distribution and operation habit are collected through eye tracking and flight control data, human experience strategy priori is formed through time sequence feature extraction and imitation learning, and is adaptively optimized under a reinforcement learning framework in combination with multi-target rewards such as coverage, safety, energy efficiency and behavior consistency to obtain a transferable autonomous navigation strategy; obstacle avoidance flight is realized in a manner combining global task route planning and local rolling track control, and automatically switches to a local safety strategy when communication or positioning is abnormal. Compared with the prior art, the present application can stably complete bamboo forest autonomous inspection in a complex environment, and significantly improves operation safety, running efficiency and information acquisition quality.

The invention relates to a small-space large-torque pre-tightening method and device based on visual navigation. The small-space large-torque pre-tightening device comprises a supporting seat and a cylindrical straight gear set with the axis perpendicular to the supporting seat. The cylindrical gear set comprises a driving gear meshed with the interior of the driven gear ring and an inner hexagonal wrench caliper integrated with the driven gear ring, and a photoelectric baffle shields a photoelectric switch to enable a motor to be powered off so as to guarantee that the wrench caliper does not exceed the stroke limit position of the wrench caliper. According to the large-torque pre-tightening device, traditional outer meshing design is replaced by gear and gear ring inner meshing design, so that the space occupied by the large-torque pre-tightening device is reduced, assembling and pre-tightening in a narrow space are achieved, and pre-tightening torques of a plurality of bolts tend to be consistent and accurate through driving and controlling of the motor; a mechanical arm is driven through visual navigation to convey the large-torque pre-tightening device to a position where manual assembly operation cannot be measured and reached, and collision interference with other equipment and parts in a vehicle is prevented.

1. The name of the design product: AI visual navigation controller (AN-V100). 2. The use of the design product: The product is an AI visual navigation device for autonomous visual navigation and path planning of agricultural machinery. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: combination state diagram.

The invention relates to the technical field of interventional therapy, and particularly discloses a percutaneous puncture navigation system which comprises a camera module and a display module, the display module displays a main picture shot by the camera module, and an electronic protractor picture is suspended on the main picture as a floating layer; a puncture layer S, a puncture point A, a puncture angle alpha, a puncture depth D and a puncture target point T of a focus area of a patient are determined, a camera module is arranged on one side of the patient in the scanning direction, and the camera module aims at the body surface of the focus area to shoot, so that a round point O in a picture of the electronic protractor coincides with the puncture point A of the body surface of the patient; a datum line in an electronic protractor picture is parallel to the horizontal plane; the puncture needle coincides with the angle line of the angle alpha in the picture of the electronic protractor, and the puncture needle enters the depth D along the angle line alpha of the electronic protractor and then reaches the puncture target point T. The puncture cloth needle arranged as the puncture needle achieves the visual navigation function, reduces the puncture difficulty, improves the puncture precision and reduces the puncture scanning frequency.

This utility model relates to the field of visual navigation technology for unmanned aerial vehicles (UAVs), specifically, to a visual flight navigation system. It involves setting a navigation module for return-to-home use under normal conditions to provide location information, and setting up a LiDAR and camera for combined use. When the navigation module signal is lost, the LiDAR and camera are used to achieve the return-to-home objective, and the navigation module's PPS signal (time synchronization signal) is sent to the LiDAR module to achieve time synchronization.

The invention discloses a method and a system for realizing low-sight-distance visual navigation of a photovoltaic cleaning robot with a low chassis height. A downward-looking fisheye camera module is arranged at the bottom of the robot to obtain an image of a photovoltaic panel under a vehicle body, a fisheye image correction algorithm is used for conducting distortion removal on a distorted image, an edge distorted area is cut, and a low-distortion core area is obtained; carrying out binarization and edge identification on the core area, and extracting and screening a feature edge line which can be used as a vehicle body motion reference; and resolving the slope and intercept of the characteristic edge line under the condition that the image coordinate system is aligned with the coordinates of the vehicle body to obtain a transverse deviation delta X and a course deflection angle delta beta of the vehicle body relative to the reference line, and sending the transverse deviation delta X and the course deflection angle delta beta to a main control system in real time for closed-loop deviation correction. The system can guarantee image quality through linkage of a light supplementing device and exposure / gain under the condition of low illumination at night, and can output confidence and an abnormal state for degradation control, so that high-precision tracking navigation is realized on the premise of not lifting a chassis.

The invention discloses an unmanned automatic rice transplanting vehicle based on a self-driving instrument and visual navigation in the technical field of rice transplanting machines, and the automatic rice transplanting vehicle comprises a rice transplanting vehicle body which is provided with a frame, and an industrial personal computer, a gear operating lever, an accelerator assembly, a brake assembly and a steering wheel which are arranged on the frame, a gear adjusting piece for rotating the gear operating rod, an accelerator adjusting piece for rotating the accelerator assembly, a brake adjusting piece for rotating the brake assembly and a self-driving instrument for rotating the steering wheel are arranged on the rice transplanting vehicle body. Navigation data are provided for the industrial personal computer through the self-driving instrument, the industrial personal computer controls the rice transplanting vehicle to run along the rice transplanting path through the self-driving instrument according to the predefined rice transplanting path, automatically turns around when encountering a ridge, meanwhile, controls the rice transplanting platform to be lifted up, and controls the rice transplanting platform to be put down after turning around is completed, automatic driving is achieved, operation is convenient, and the rice transplanting efficiency is improved. Therefore, the automation degree of the rice transplanter is improved.

A visual navigation method for robot swarms based on differentiable physics and agent models is proposed. A high-level navigation policy network processes raw image information acquired from a depth camera, using a deep learning model to understand the environmental geometry, identify obstacles and dynamic agents, and output velocity commands for obstacle avoidance, collision avoidance, and target navigation. A low-level motion policy network translates these velocity commands into joint position and torque commands for the quadruped robot's legs, generating stable and flexible body movements to execute velocity tracking strategies. After training using a forward-backward asymmetric optimization framework, swarm control is achieved in the online phase. This invention improves training sample efficiency and policy transferability in scenarios relying solely on local visual perception, while overcoming the curse of dimensionality in multi-agent learning. It can be flexibly extended to large-scale robot swarms and achieves zero-sample transfer from simulation to real-world scenarios.

The invention discloses a monocular visual odometer system and method based on PV-BEV fusion and dense flow supervision, through a PV-BEV double-branch architecture, correlation volume is calculated in a PE space, 5-degree-of-freedom motion information is reserved, and then shared LSS parameters are projected to a BEV space to be fused with BEV native correlation, so that information loss of a traditional BEV method is effectively compensated. Meanwhile, a dense BEV optical flow field is directly constructed from a three-degree-of-freedom pose truth value by using the measurement scale characteristic represented by BEV, a pixel-level supervision signal is provided, and the limitation of sparse pose supervision is overcome. In cooperation with an enhanced rotation sampling strategy, the system achieves the first advanced performance on a plurality of data sets, the relative translation error is reduced by more than 40% compared with an existing BEV method, and extra labeling such as depth and optical flow is not needed. The method is particularly suitable for low-cost and high-precision visual navigation of intelligent traffic scenes such as automatic driving and logistics robots.

The invention relates to the technical field of automobile diagnosis, and discloses a vehicle diagnosis navigation method, device and equipment and a readable storage medium, and the method comprises the steps: obtaining vehicle environment information collected after a communication connection with a target vehicle is established; receiving diagnosis instruction information input by a user, fusing the diagnosis instruction information with the vehicle environment information, and constructing to-be-analyzed data; processing the to-be-analyzed data based on a preset semantic navigation model, and determining a target function instruction and path information corresponding to the to-be-analyzed data; and generating visual navigation path information based on the target function instruction and the path information. According to the method, the automobile diagnosis operation process is effectively simplified, and the use convenience and the working efficiency of maintenance personnel in a multi-brand automobile diagnosis scene are remarkably improved.

This invention relates to a markerless real-time registration and tracking method and system for mandibular surgery. The method includes the following steps: the patient performs multiple opening and closing movements of the upper and lower jaws while the robotic arm is fixed, and the system synchronously records the changes in the angles of each joint of the robotic arm; based on the forward kinematics of the robotic arm, the continuous motion trajectory of the distal end of the jaw in space is calculated to form an actual jawbone trajectory point cloud; a preoperative image model is formed using the actual jawbone trajectory point cloud, a simulated jawbone trajectory is constructed in the preoperative image model, and the actual trajectory is aligned using the ICP registration algorithm to achieve automatic registration; the system establishes a mapping between angle changes and the spatial posture of the jawbone to achieve dynamic tracking and visual navigation during surgery. This invention solves the problems of large errors, high complexity, many limitations, and low efficiency in preoperative image data registration technology during maxillofacial surgery, avoids the need for preoperative implantation of markers, and achieves high-precision registration, high automation, and strong real-time performance under natural conditions.