Patent ID: 11953903
Assignee: STAR INSTITUTE OF INTELLIGENT SYSTEMS
Field: Control (Instruments)
Classification: CPC G  H | IPC G  H

Claim 3:
4. The neural network-based method for calibration and localization of an indoor inspection robot according to claim 3, wherein the establishing an odometry error model E in step S500 specifically comprises:
S510: assuming that the neural network structurally comprises an input layer having q neurons, a hidden layer having l neurons, and an output layer having m neurons, connections between the neurons of the input layer and the neurons of the hidden layer each have a weight value of wij, connections between the neurons of the hidden layer and the neurons of the output layer each have a weight value of wjk, the hidden layer has a bias term of a, and the output layer has a bias term of b;
S520: taking the GLM as an input of the neural network to obtain an output result of the hidden layer:, H
       j
      
      =
      
       
        
         f
         ⁡
         (
         
          
           
            ∑
            
             i
             =
             1
            
            q
           
           
            
             w
             ij
            
            ⁢
            
             p
             i
            
           
          
          -
          
           a
           j
          
         
         )
        
        ⁢
           
        j
       
       =
       1
      
     
     ,
     2
     ,
     …
       
     ,
     l
    
   
   
    
     (
     
      5
      -
      2
     
     )
    
   
  
 

wherein, pi represents a vector for coordinates (xn-1,yn-1) and (xn,yn) of the input GLM, f represents an activation function, and i={1, 2, . . . , q}, j={1, 2, . . . , l}; and
a result from the output layer of the neural network is:, O
     =
     
      
       
        
         ∑
         
          j
          =
          1
         
         l
        
        
         
          H
          i
         
         ⁢
         
          w
          jk
         
        
       
       -
       
        b
        k
       
      
      =
      
       {
       
        
         
          x
          ˆ
         
         1
        
        ,
        
         
          x
          ˆ
         
         2
        
        ,
        …
          
        ,
        
         
          x
          ˆ
         
         n
        
        ,
        
         
          y
          ˆ
         
         1
        
        ,
        
         
          y
          ˆ
         
         2
        
        ,
        …
          
        ,
        
         
          y
          ˆ
         
         n
        
       
       }
      
     
    
   
   
    
     (
     
      5
      -
      5
     
     )
    
   
  
 

 
  
   k
   =
   1
  
  ,
  2
  ,
  
   …
   ⁢
     
   m, and
S530: taking the result from the output layer of the neural network at the tth moment and the actual path at a present moment as an input of a loss function to establish the odometry error model E.