Multi-modal sensor online calibration method based on dynamic environment self-adaption

Abstract

The invention relates to a multi-modal sensor online calibration method based on dynamic environment self-adaption, which comprises the following steps: constructing a memory enhanced scene understanding network architecture, embedding a topology memory unit in a Segment Anything Model basic framework, and constructing a double-layer knowledge solidification mechanism; performing construction and feature extraction of a differential geometric field, and proposing a continuous geometric representation scheme based on a neural radiation field; a decoupling type cross-modal adversarial optimization mechanism is introduced, and a calibration task is decomposed into a geometric constraint and semantic association dual-channel optimization problem; a Bayesian-genetic hybrid optimization framework is provided for an environment-adaptive hybrid optimization engine, and global exploration and local fine tuning of a parameter space are realized. According to the invention, the automatic driving function with high efficiency, accuracy and high adaptability is realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of multi-sensor fusion in autonomous driving, and specifically relates to an external parameter calibration system that combines neural radiation field characterization with online learning, which is suitable for real-time calibration in dynamic and complex environments. Background Art

[0002] The current multi-sensor calibration technology for autonomous driving faces severe challenges in dynamic adaptability and environmental robustness. Although the traditional offline calibration method based on static targets (such as Zhang's calibration method) can achieve an accuracy of ±0.05° in a laboratory environment, it is affected by factors such as mechanical vibration and temperature deformation in actual driving. The external parameter error will accumulate at a rate of 0.02° per hour, resulting in a spatial alignment failure rate of 23% between the millimeter-wave radar and the camera after 72 hours (Bosch 2022 Technical Report). Although online calibration tools such as Kalibr introduce IMU motion models, their optimization framework that relies on manual feature points still has feature matching inaccuracy problems in low-texture or strong light interference scenarios. A 2021 study showed that its calibration failure rate in urban tunnel scenarios was as high as 37%.

[0003] Although data-driven methods represented by deep learning avoid artificial feature design, they are limited by the domain offset between the distribution of training data and the real scene. Waymo's measured data in 2023 showed that rainy and foggy weather can cause scattering distortion of the intensity value of the lidar point cloud, causing the pitch angle calibration error of the existing model to surge from 0.05° to 0.82%. At the same time, the refraction effect of water droplets in the camera image will cause the semantic segmentation model to produce an error rate of more than 15%. The coupling of the two leads to a break in the cross-modal feature association. On the other hand, although discretization processing such as point cloud voxelization can improve computing efficiency, recent studies have quantified that it will cause 12%-15% of curvature details to be lost, resulting in a decrease in the credibility of the micro-geometric features required for calibration (such as normal vectors and surface curvature), which ultimately affects the convergence accuracy of the external parameter matrix.

[0004] Although the emerging neural radiance field (NeRF) technology has demonstrated the advantage of continuous characterization in 3D reconstruction, its engineering application in sensor calibration still has significant bottlenecks. Experiments by the MIT CSAIL team in 2023 showed that when the original NeRF was used directly for calibration, single-frame rendering took more than 30 seconds, and the lag in radiation field updates in dynamic scenes caused the external parameter error to expand to 2.3 times that of traditional methods. The deeper contradiction lies in the fact that the existing NeRF framework lacks a cross-modal consistency constraint mechanism and cannot synchronously optimize the geometric-semantic association between the lidar point cloud and the camera image, resulting in systematic deviations in the calibration results in complex lighting and motion blur scenes. The above technical defects jointly restrict the all-weather reliability of autonomous driving systems in dynamic environments, and breakthroughs are urgently needed through innovative methods. Summary of the invention

[0005] Technical problems to be solved: Aiming at the problems of insufficient adaptability, low precision, and low computational efficiency in unsupervised external parameter calibration in the existing technology under dynamic environments, the present invention proposes an online calibration method for multi-modal sensors based on dynamic environment adaptability. When dealing with complex dynamic scenarios, the existing technology often faces technical bottlenecks such as long scene adaptation time, difficulty in simultaneously optimizing geometric and semantic consistency, and environmental interference factors affecting calibration accuracy. Specifically, traditional methods are difficult to quickly adapt to new scenarios in dynamic environments, resulting in a decrease in calibration accuracy; in the process of multi-modal data fusion, the decoupling and collaborative optimization of geometric and semantic information are not yet perfect; in addition, the interference problems of environmental factors such as light changes, motion blur, and precipitation during the calibration process have not been effectively solved. The present invention realizes high-precision and high-efficiency unsupervised external parameter calibration in dynamic environments through four innovative modules: a memory-enhanced scene understanding network, differential geometric field construction and feature extraction, decoupled cross-modal adversarial optimization mechanism, and environment-adaptive hybrid optimization engine, effectively solving the above technical problems.

[0006] Technical solutions:

[0007] Aiming at the existing technical bottlenecks, the present invention proposes an online calibration method for multi-modal sensors based on dynamic environment adaptability, and realizes high-precision calibration in dynamic environments by constructing a multi-modal joint optimization framework, including the synergistic effect of four innovative modules:

[0008] S1. Construct a memory-enhanced scene understanding network architecture, embed a topological memory unit in the basic framework of Segment Anything Model (SAM), and construct a two-layer knowledge solidification mechanism. The specific implementation includes:

[0009] Insert a d-dimensional programmable memory matrix (d = 512) into the L-th layer (where L = 3, 6, 9) of the Transformer encoder, and use a gated recurrent mechanism to dynamically update the scene prototype features. The memory unit interacts with the current features through the attention weight calculation formula

[0010]

[0011] where Q is the query matrix, K is the key matrix, d is the feature dimension, and softmax is the normalization function; the storage frequency is set to f (f = 10Hz), the storage capacity is N (N = 5000 feature vectors), and the least recently used (LRU) elimination strategy is supported to maintain spatial consistency

[0012] Secondly, a cross-domain meta-learning optimizer is designed. The MAML framework is adopted to achieve rapid parameter adaptation. The inner loop performs 3 gradient descents with a learning rate of 0.003, and the outer loop calculates the meta-gradient through the second derivative. Experiments show that this mechanism can reduce the new scenario adaptation time from 15 minutes of traditional methods to 23 seconds;

[0013] Finally, a scene complexity awareness module is introduced. By calculating the image information entropy

[0014] H(I t ) = -∑p(x)logp(x)

[0015] where I t is the image at the t-th moment, and p(x) is the image pixel probability distribution. At the same time, the point cloud curvature variance is calculated and the participation intensity of the memory unit is adjusted in real time in a weighted sum manner. The weight formula is

[0016]

[0017] where λ is the weight coefficient and tanh is the hyperbolic tangent function.

[0018] S2. Regarding the construction and feature extraction of the differential geometric field, a continuous geometric representation scheme based on the neural radiance field is proposed: on the one hand, an L-layer MLP network (L = 8) is constructed to achieve implicit encoding of the point cloud. The input three-dimensional coordinates are extended to d pos dimensions (d pos = 63) through n-order (n = 5) position encoding, and a d feat -dimensional (d feat = 256) feature vector and density value σ are output. The weight initialization method is Xavier initialization, the optimizer is the Adam optimizer, and the loss function includes the rendering loss L rgb , which measures the color reconstruction error; the depth consistency loss L d , which ensures the consistency between the depth prediction and the true depth; the curvature smoothing term where λ C = 0.1 is the balance coefficient, is the density gradient field.

[0019] A hybrid acceleration engine is developed, which combines voxel hashing and spatial domain pruning algorithms to increase the single-frame point cloud processing speed to T (T = 45ms) / frame; a motion compensation module is designed for dynamic objects to construct spatio-temporal continuity constraints through adjacent frame optical flow estimation

[0020] L flow = ||F t - F t-1 || Huber

[0021] where F t is the optical flow field at time t, and ||·|| Huner is the Huber norm, which is used for robust regression;

[0022] Extract multi-scale geometric features, perform K (K = 128) differentiable samplings along each laser ray, and calculate the density gradient field As the surface normal vector estimate, aggregate the curvature parameters within the neighborhood with an aggregation radius r (r = 0.1 m)

[0023] k = (λ1 + λ2) / 2

[0024] where λ1 and λ2 are the principal curvatures, and generate a radiation field representation with geometric attributes.

[0025] S3, introduce a decoupled cross-modal adversarial optimization mechanism, and decompose the calibration task into a two-channel optimization problem of geometric constraint and semantic association:

[0026] The geometric manifold projection network adopts an L-layer (L = 5) 3D convolutional structure, takes the point cloud density field as input, and outputs a d-dimensional (d = 128) manifold embedding vector; the network introduces a curvature-aware attention module, and calculates the local curvature correlation score through the learnable matrix W q 、W k Calculate the local curvature correlation score

[0027]

[0028] where Q i and K j are the query vector and the key vector respectively, and are linearly transformed by W q and W k ;

[0029] The semantic cross discriminator constructs a two-branch structure: the image branch uses U-Net to extract multi-scale features, and the point cloud branch aggregates neighborhood information through graph convolution; the two interact through the multi-head attention mechanism to calculate the cross-modal similarity matrix

[0030]

[0031] where Q img and K pc are the feature vectors of the image and the point cloud respectively, is the scaling factor, d is the feature dimension; the discriminant score D adv ;

[0032] Design an improved Wasserstein distance loss function:

[0033] L total = α(E[D(G)] - E[D(R)]) + βL grad + γLcycle

[0034] Among them, α is the geometric constraint strength coefficient, β is the gradient penalty coefficient, γ is used for cyclic consistency constraint, E[·] represents the expected value, G and R are the generated sample and the real sample respectively, and L grad is the gradient penalty term, and L cycle is the cyclic consistency loss; through the alternating optimization strategy, the geometric accuracy and semantic consistency are improved synchronously.

[0035] S4. Regarding the hybrid optimization engine for environment adaptation, a Bayesian-genetic hybrid optimization framework is proposed to achieve global exploration and local fine-tuning of the parameter space:

[0036] First, a Gaussian process prior model is constructed using the Bayesian inference module, and the covariance kernel adopts the Matern 5 / 2 function:

[0037]

[0038] , where r = ||x - x'|| / l, l is the length scale, and the dynamic adjustment range is [L min , L max (L min = 0.8, L max = 1.2). Candidate points are selected through the expected improvement criterion, and the posterior distribution is updated by retaining the top P% (P = 5) samples in each iteration.

[0039] Second, the adaptive genetic algorithm sets the population size N (N = 100), and the elite retention rate is p elit (p elit = 15%), the crossover probability p c decays dynamically according to

[0040]

[0041] , where p c0 = 0.5, p c1 = 0.3, g is the current iteration step, and G is the total number of iteration steps; the mutation probability p m increases according to

[0042]

[0043] , where p m0 = 0.1, p m1 = 0.2, to balance early exploration and late convergence; the individual fitness is determined by the calibration residual

[0044] E = ||T pred - T gt ||2

[0045] and the environmental factor

[0046] E env = ω1·Rain + ω2·Blur

[0047] Jointly determined, where Rain is the precipitation intensity, Blur is the degree of motion blur, ω1 = 0.3, and ω1 = 0.5;

[0048] The environmental perception unit, which collects the light L, motion blur Blur, and precipitation parameter Rain in real time, constructs a 3D influence vector v env = [L, Blur, Rain], adjusts the optimization direction through a gating mechanism, and increases the geometric constraint weight to α max (α max = 0.9) under heavy precipitation, and increases the mutation rate of the genetic algorithm to p max (p max = 0.3) in high-dynamic scenarios to achieve robust calibration under complex working conditions.

[0049] Beneficial effects:

[0050] First, by constructing a memory-enhanced scene understanding network and a differential geometric field, the present invention significantly improves the accuracy and efficiency of unsupervised extrinsic parameter calibration in a dynamic environment. On the KITTI open dataset, the present invention achieves a translation error of 0.018 meters (a 72% improvement compared to traditional methods) and a rotation error of 0.12 degrees (a 65% improvement), while increasing the single-frame point cloud processing speed to 45 milliseconds / frame, greatly improving the real-time performance and adaptability of the system.

[0051] Second, the environment-adaptive hybrid optimization engine proposed by the present invention combines Bayesian inference and genetic algorithms, effectively enhancing the robustness of the system in complex environments. In a heavy rain scenario, the system maintains an error volatility below 8%, significantly superior to existing technical solutions, ensuring stable calibration performance in adverse weather and high-dynamic scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a system architecture diagram of an online calibration and optimization method for multi-modal sensors based on dynamic environment adaptation. DETAILED DESCRIPTION OF THE INVENTION

[0053] The following embodiments can enable those skilled in the art to understand the present invention more comprehensively, but do not limit the present invention in any way.

[0054] Please refer to Figure 1, This technical document details the development process of an autonomous driving system, which consists of four main stages: Memory-Augmented Segmentation Network, Neural Radiance Field Construction, Decoupled Adversarial Optimization Process, and Environment-Adaptive Hybrid Optimization Execution. Each stage employs advanced technical means to achieve efficient, accurate, and adaptable autonomous driving functions. The document will elaborate on the design and implementation of each stage step by step to ensure the coordinated operation of the overall system.

[0055] (I) Deployment and Training of Memory-Augmented Segmentation Network

[0056] The core objective of this stage is to achieve rapid adaptation of scene understanding through a dynamic memory mechanism. The specific implementation includes three levels:

[0057] (1) The first level is based on the open-source SAM-Base model (Vision Transformer-Hybrid architecture, input resolution H×W, where H = W = 1024), and programmable memory units are injected into the key levels of the Transformer encoder. Layers L1, L2, and L3 (L1 = 3, L2 = 6, L3 = 9) are selected for transformation, mainly considering:

[0058] Layer L1 corresponds to the shallow edge feature extraction stage, and the memory unit can capture basic geometric patterns;

[0059] Layer L2 is in the middle-level semantic understanding stage, storing the typical topological structures of target objects;

[0060] Layer L3 corresponds to high-level scene parsing, memorizing global context association rules.

[0061] Each memory unit is designed as a d-dimensional dynamic storage matrix (d = 512), and the capacity is set to C feature vectors (C = 300). The gated update mechanism adopts a bilinear fusion strategy, and the gated weight matrix W g with dimension d g ×d g (d g = 1024), and the fusion coefficient f g ∈[0,1] generated by the Sigmoid function. This design enables the system to update the memory content at a frequency f (f = 10Hz) to ensure the freshness of features in a dynamic scene.

[0062] (2) The second level is the cross-domain meta-learning training process. To solve the problem of rapid adaptation to new scenes, an improved MAML training framework is adopted:

[0063] Inner loop stage: Randomly select N scene prototypes from the memory bank to construct the support set (N = 50), and perform fast fine-tuning. Set the learning rate η1 (η1 = 0.001), and update the local parameters through K times (K = 3) of gradient descent. The loss function is a weighted combination of the segmentation accuracy and the memory reconstruction loss (weight ratio 7:3).

[0064] Outer loop stage: Calculate the second derivative based on the meta-loss of the validation set, update the global meta-parameters with the learning rate η2 (η2 = 0.0001), and introduce Nesterov momentum acceleration to effectively avoid local optima.

[0065] The memory eviction strategy adopts the LRU (Least Recently Used) algorithm. When the storage capacity exceeds the preset threshold S max when, preferentially remove the low-frequency features that have not been called within the last T minutes (T = 30). This mechanism controls the memory occupancy within M (M = 1.2GB) while maintaining the integrity of the high-frequency features γ (γ = 0.95).

[0066] (3) The third level is dynamic environment perception and adjustment. Specifically, deploy a scene complexity quantification module to calculate in real time:

[0067] Image information entropy: Divide the input image into P×P blocks (P = 16), and calculate the Shannon entropy value of the gray histogram of each block. The formula is

[0068]

[0069] , reflecting the scene texture complexity, where I t is the image at the t-th moment, and p(x) is the image pixel probability distribution.

[0070] Point cloud curvature variance: Calculate the principal curvature k for each point cloud cluster, and evaluate the surface undulation degree through

[0071]

[0072] where K is the number of points in the point cloud cluster, K k is the principal curvature, is the average value of the principal curvature.

[0073] Dynamically adjust the memory participation degree λ through the PID controller:

[0074] When the vehicle enters the highway scene (H(I t ) < H high , ), the value of λ automatically drops to λ min (λ min = 0.3), reducing the storage of redundant features;

[0075] In the complex urban road conditions (H(I t )<H low , ) when, λ is increased to λ max (λ max = 0.8), activating the high-frequency update mode.

[0076] Among them, H high = 2.5, H low = 4.0, σ high = 0.1, σ low = 0.25.

[0077] (II) Construction and acceleration of neural radiance fields

[0078] This stage focuses on constructing a high-fidelity environmental geometry representation and achieving real-time inference performance. The specific implementation includes:

[0079] (1) First is the implicit neural field modeling. Design an L-layer fully connected network (L = 8), with N neurons in each layer (N = 256). Configure the balance between accuracy and convergence speed through a phased activation function:

[0080] The first M layers (M = 4) use ReLU activation to quickly capture low-frequency geometric features. The middle K layers (K = 3) use the Softplus function (β = 100) to enhance the ability to express high-frequency details. The last layer uses the Sigmoid function to output the density value σ ∈ [0, 1], representing the spatial occupancy probability.

[0081] The position encoding uses an m-order (m = 5) Fourier expansion to map the three-dimensional coordinates (x, y, z) to a D-dimensional (D = 63) feature space. The mathematical expression is:

[0082] γ(x) = [sin(2 0 πx), cos(2 0 πx), …, sin(2 4 πx), cos(2 4 πx)] (3)

[0083] Among them, x is any one dimension in the three-dimensional coordinates, and γ(x) is the Fourier expansion vector of this dimension. The direction encoding uses an n-order (n = 2) expansion to expand the viewing vector to 2n + 1 dimensions, enhancing the ability to model the material reflection characteristics.

[0084] (2) Secondly, it involves the development of a hybrid acceleration engine and the construction of a multi-resolution voxel hash table. The specific parameter configuration is shown in Table 1:

[0085] Table 1 Specific parameter configuration of the resolution voxel hash table

[0086]

[0087] Combined with the spatial domain pruning algorithm, parallel query is implemented in the CUDA kernel: for each sampling point, rough positioning is first performed at level 1 (taking 0.2 ms). Subsequently, the subsequent query levels are automatically selected according to the density gradient, reducing 60% of the invalid calculations on average.

[0088] (3) Finally, there is the dynamic scene processing module, which designs spatio-temporal continuity constraints for moving objects:

[0089] Estimate the object motion vector through the optical flow field of adjacent frames

[0090] V = (Δx, Δy, Δz) (4)

[0091] where V represents the object motion vector, and Δx, Δy, and Δz represent the displacement change amounts of the object on the x, y, and z coordinate axes respectively.

[0092] Construct the motion compensation loss term:

[0093] L flow = ||F t - F t-1 || Huber (5)

[0094] where F t and F t-1 represent the feature maps of the t-th frame and the (t - 1)-th frame respectively, and ||·|| Huber represents the Huber norm, which is used to measure the difference between feature maps.

[0095] Set the motion sensitivity coefficient

[0096] η = 0.1·||V|| 2 (6), dynamically adjust the rendering weight, η is the motion sensitivity coefficient, and ||V|| represents the norm of the motion vector V.

[0097] During the training stage, a sampling strategy of R = 1024 rays / batch is adopted, and each ray performs S = 128 hierarchical samplings (S c = 32 coarse samplings + S f = 96 fine samplings based on the importance distribution), and the model convergence is completed in 12 hours on the NVIDIA RTX3090 graphics card.

[0098] (III) Decoupled adversarial optimization process

[0099] In this stage, the collaborative optimization of cross-modal calibration parameters is realized, and the specific implementation is divided into three subsystems:

[0100] (1) First is the geometric manifold generation network, which constructs a 5-layer residual structure generator:

[0101] Input layer: A 3D convolutional kernel of 7×7×7 is used, with the number of channels C1 = 64 and the stride s = 2 to extract macroscopic geometric features.

[0102] ResBlock-1: A convolutional kernel of 5×5×5 is adopted, with the number of channels C2 = 128, and deformable convolution is introduced to adapt to irregular point clouds.

[0103] ResBlock-2: A convolutional kernel of 5×5×5 is adopted, with the number of channels C3 = 256, and the curvature attention mechanism is integrated.

[0104] ResBlock-3: A convolutional kernel of 3×3×3 is adopted, with the number of channels C4 = 512, and scene semantic priors are fused.

[0105] Output layer: Transposed convolution upsampling, 1024 channels, to generate a 128-dimensional manifold embedding.

[0106] The curvature attention calculation formula is:

[0107]

[0108] , where k i and k j represent the curvature vectors of the i-th and j-th points respectively, and N(i) represents the neighborhood of the i-th point. The neighborhood radius r = 15 cm to ensure the coherence of local surface characteristics.

[0109] (2) Secondly, a cross-modal discriminative network is constructed, and a dual-branch discriminator architecture is designed:

[0110] Geometry branch: Use 3 layers of 3D convolution (kernel sizes 7×7×7, 5×5×5, 3×3×3, and the number of channels are C6 = 32, C7 = 64, C8 = 128 respectively) to output the geometric consistency score.

[0111] Semantic branch: U-Net structure (encoder downsampling rate 4× / 8× / 16×, decoder corresponding upsampling), outputting a C9 = 256-dimensional semantic embedding.

[0112] The cross-modal interaction module calculates the similarity matrix:

[0113]

[0114] , where Q img is the [CLS] token from the image branch, K pc is the point cloud map feature, and d = 256 is the normalization factor.

[0115] (3) Finally, a progressive training strategy is adopted, using a two-stage optimization scheme:

[0116] Stage 1 (the first E1 = 50 rounds): Fix the generator and strengthen the discriminator with a learning rate of η1 = 0.0002, focusing on improving the cross-modal alignment ability.

[0117] Stage 2 (from E1 = 50 rounds to E2 = 200 rounds): Implement a differential learning rate strategy, with the generator learning rate η2 = 0.0001 and the discriminator learning rate η3 = 0.0004. Balance the training stability through the Wasserstein distance (weight α = 0.7) and the gradient penalty term (λ = 10).

[0118] (4) Execution of Environment Adaptive Hybrid Optimization

[0119] In this stage, Bayesian optimization and genetic algorithm are integrated to achieve intelligent adjustment of calibration parameters:

[0120] (1) Introduce a Bayesian inference engine to construct a Gaussian process prior model. The core components include:

[0121] The covariance kernel function is the Matern 5 / 2 kernel, and the expression is

[0122]

[0123] where, l is the length scale parameter, and for the dynamic length scale adjustment, according to the historical optimization trajectory, according to the rule

[0124]

[0125] achieve the balance between exploration and development, where is the gradient of energy with respect to the length scale.

[0126] (2) Improved genetic algorithm, specifically involving the parameter space partitioned as:

[0127] The translation component is x ∈ [-a, a], where a = 1.5m, b = 0.5m, c = 0.3m;

[0128] The rotation components are roll / pitch ∈ [-d, d], yaw ∈ [-e, e], where d = 5, re = 15.

[0129] Implementing the adaptive genetic strategy has three processes. On the one hand, for the crossover probability, it linearly decays from the initial value to The formula is:

[0130]

[0131] where, Δp c = 0.3, g is the current generation number, and G is the total generation number. On the other hand, for the mutation probability, it decays from the initial value Increase to The formula is:

[0132]

[0133] Among them, Δp m = 0.2. Finally, it is reserved for the elites, and 15% of the best individuals are reserved in each generation to maintain the population diversity.

[0134] (3) Finally, for multi-modal environmental perception, a sensor fusion unit needs to be deployed, and the key parameters are shown in Table 2.

[0135] Table 2 Key Parameters of Sensor Fusion Unit

[0136]

[0137] The environment-parameter mapping logic is shown in Table 3.

[0138] Table 3 Environment-Parameter Mapping Logic

[0139]

[0140] In addition, regarding the hardware deployment plan: Deploy the NVIDIA Orin computing platform, implemented through the TensorRT engine, memory unit access latency: <1.2ms; radiation field inference speed: 45ms / frame; time-consuming for a single iteration of hybrid optimization: <8ms.

[0141] The above is only the preferred implementation mode of the present invention. The protection scope of the present invention is not limited to the above embodiments. All technical solutions falling within the idea of the present invention belong to the protection scope of the present invention. It should be noted that for those of ordinary skill in the art, several improvements and refinements made without departing from the principle of the present invention should be regarded as within the protection scope of the present invention.

Claims

1. A multimodal sensor online calibration method based on dynamic environment adaptation, characterized in that: Includes steps: S1, preprocessing the images and point cloud data input by the multimodal sensor, extracting multimodal features and constructing a memory-enhanced scene understanding network. The constructed memory-enhanced scene understanding network architecture encodes scene features through topological memory units and establishes a scene representation model; S2, input the scene features obtained in S1 into the differential geometry field construction module, calculate the geometric properties of the scene, extract continuous geometric features, and use the constructed differential geometry field to realize the three-dimensional geometric modeling of the scene through the neural radiation field method; S3, decouples and optimizes the geometric features obtained in S2 with multimodal sensor data to separate geometric constraints from semantic associations. The introduced decoupled cross-modal adversarial optimization process uses cross-modal adversarial training to optimize geometric accuracy and semantic consistency respectively. S4, input the optimized parameters into the Bayesian-Genetic hybrid optimization engine for global search and local fine-tuning. The hybrid optimization engine realizes global exploration of the parameter space through Bayesian optimization and performs local fine-tuning through genetic algorithm to finally obtain the optimal calibration parameters.

2. The method for online calibration of a multimodal sensor based on dynamic environment adaptation according to claim 1 is characterized in that: Step S1 comprises, A d-dimensional programmable memory matrix is ​​inserted into the Lth layer of the Transformer encoder, and a gated loop mechanism is used to dynamically update the scene prototype features. The memory unit is calculated by the attention weight formula Interact with the current features, where Q is the query matrix, K is the key matrix, d is the feature dimension, and softmax is the normalization function; the storage frequency is set to f, the storage capacity is N, and the least recently used elimination strategy is supported to maintain spatial consistency; Design a cross-domain meta-learning optimizer, using the MAML framework to achieve fast parameter adaptation. The inner loop performs K gradient descents with a learning rate a, and the outer loop calculates the meta-gradient through the second-order derivative. Introducing the scene complexity perception module, by calculating the image information entropy H(I t )=-∑p(x)logp(x) Among them I t is the image at time t, p(x) is the probability distribution of image pixels; meanwhile, the curvature variance of the point cloud is calculated The participation intensity of the memory unit is adjusted in real time by using a weighted sum method. The weight formula is: Where λ is the weight coefficient and tanh is the hyperbolic tangent function.

3. The method for online calibration of a multimodal sensor based on dynamic environment adaptation according to claim 1 is characterized in that: Step S2 includes: Construct an L-layer MLP network to realize implicit coding of point cloud, and expand the input 3D coordinates to d through n-order position coding. pos dimension, output d feat The dimension feature vector and density value σ, the weight initialization method is Xavier initialization, the optimizer is Adam optimizer, and the loss function contains the following parts:

1. Rendering loss L rgb , measures the color reconstruction error; 2. Depth Consistency Loss L d , ensuring the consistency between depth prediction and true depth; 3. Curvature smoothing term where λ C =0.1 is the balance coefficient, is the density gradient field; Develop a hybrid acceleration engine that combines voxel hashing and spatial pruning algorithms to increase the processing speed of single-frame point clouds to T / frame; design a motion compensation module for dynamic objects and construct spatiotemporal continuity constraints through adjacent frame optical flow estimation L flow =‖F t -F t-1 ‖ Huber Among them, F t is the optical flow field at time t, ‖·‖ Huber is the Huber norm, used for robust regression; Extract multi-scale geometric features, perform K differentiable sampling along each laser ray, and calculate the density gradient field As the surface normal vector estimate, the curvature parameter in the neighborhood of the aggregation radius r k=(λ1+λ2) / 2 Among them, λ1 and λ2 are the principal curvatures, generating a radiation field representation with geometric properties.

4. The method for online calibration of a multimodal sensor based on dynamic environment adaptation according to claim 1, characterized in that: Step S3 comprises, A decoupled cross-modal adversarial optimization mechanism is introduced to decompose the calibration task into a dual-channel optimization problem of geometric constraints and semantic associations: The geometric manifold projection network adopts an L-layer 3D convolutional structure, inputs a point cloud density field, and outputs a d-dimensional manifold embedding vector; the network introduces a curvature-aware attention module, through a learnable matrix W q , W k Calculate the local curvature correlation score Where Q i and K j are query vector and key vector respectively, and W q and W k Linear transformation is obtained; The semantic cross-discriminator constructs a dual-branch structure: the image branch uses U-Net to extract multi-scale features, and the point cloud branch aggregates neighborhood information through graph convolution; the two interact through a multi-head attention mechanism to calculate the cross-modal similarity matrix Where Q img and K pc are the feature vectors of the image and point cloud respectively, is the scaling factor, d is the feature dimension; output discriminant score D adv ; Design and improve Wasserstein distance loss function: L total =α(E[D(G)]-E[D(R)])+βL grad +γL cycle Where α is the geometric constraint strength coefficient, β is the gradient penalty coefficient, γ is used for cycle consistency constraints, E[·] represents the expected value, G and R are generated samples and real samples respectively, L grad is the gradient penalty term, L cycle is the cycle consistency loss; through alternating optimization strategies, the geometric accuracy and semantic consistency are improved simultaneously.

5. The method for online calibration of a multimodal sensor based on dynamic environment adaptation according to claim 1 is characterized in that: Step S4 comprises, The Bayesian inference module is used to construct a Gaussian process prior model, and the covariance kernel uses the Matern 5 / 2 function: Where r = ‖xx′‖ / l, l is the length scale, and the dynamic adjustment range is [L min ,L max ], select candidate points by the expected improvement criterion, and retain the first P% samples in each iteration to update the posterior distribution; The adaptive genetic algorithm sets the population size N and the elite retention rate p elit , crossover probability p c according to Dynamic attenuation, where p c0 =0.5, p c1 =0.3, g is the current iteration step, G is the total number of iteration steps; Mutation probability p m according to Increasing, where p m0 =0.1, p m1 = 0.2, balancing early exploration and late convergence; individual fitness is determined by the calibration residual E=||T pred -T gt ||2 Environmental factors E env =ω1·Rain+ω2·Blur Jointly determine, where T pred is the prediction transformation matrix, T gt is the real transformation matrix, Rain is the precipitation intensity, Blur is the degree of motion blur, ω1=0.3, ω1=0.5; The environment perception unit collects the illumination L, motion blur Blur, and precipitation parameters Rain in real time to construct a 3D influence vector v env = [L,Blur,Rain], adjust the optimization direction through the gating mechanism, and increase the geometric constraint weight to α in heavy precipitation. max , improve the genetic algorithm mutation rate to p in high dynamic scenes max , achieving robust calibration under complex working conditions.

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