Abstract:
An optical navigation system, e.g. optical mouse, determines optical navigation quality by examining some or all of the photosensors of a frame or monitoring intermediate or final results of the motion estimation system. The optical navigation quality is then used to determine the navigation output produced by the optical navigation system.

Description:
BACKGROUND 
     Optical navigation sensors typically are used for computer input devices, e.g. optical mice. Optical navigation sensors detect motion by comparing successive patterns of light, e.g. “frames”, received by an array of photosensors. The optical navigation sensor then estimates which possible motion of the mouse is most likely to have occurred based on the relationship between the current frame and a previous frame. 
     However, the likelihood that the motion estimate from the optical navigation sensor is correct depends on the quality of the frames that are compared. High-quality frames exhibit distinct features that can be tracked from frame to frame. Low-quality frames have few distinct features. 
     Whenever the frame quality is low, the motion estimation by the optical navigation sensor may not reflect the motion of the optical sensor. Low quality frames can occur for several reasons. If the photosensors are exposed to too much light, then all of the photosensors may saturate at the maximum value. If the photosensors are exposed to insufficient light, then all of the photosensors may remain at the minimum value. If the surface over which the mouse operates lacks distinct features, then navigation is difficult. If the optical sensor is too far from the surface, then any distinctive features may be out of focus. If the optical sensor moves too quickly, there may be insufficient position overlap between frames for accurate motion tracking. 
     SUMMARY 
     In the present invention, an optical navigation system, e.g. optical mouse, determines optical navigation quality by examining some or all of the photosensor values of a frame or monitoring intermediate or final results of the motion estimation system. The optical navigation quality is used to select the navigation output produced by the optical navigation system from two or more alternatives. 
     The navigation system includes a photosensor array receives reflected light from a surface and generates an analog signal indicative of the reflected light. An analog to digital converter receives the analog signal and generates a current frame signal indicative of the reflected light. The frame memory stores a previous frame signal. A motion estimation circuit that receives the two signals, indicative of current frame and previous frame, and generates an output signal indicative of the optical sensor motion. A synthetic motion signal is selected if the optical navigation quality is poor. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  shows a navigation system according to the present invention. 
         FIG. 2  is a block diagram of the optical navigator shown in  FIG. 1 . 
         FIGS. 3A–C  illustrate optical navigator outputs from which the Optical Quality Estimator may receive data. 
     
    
    
     DETAILED DESCRIPTION 
     The present invention discloses a navigation device, e.g. optical mouse that eliminates spurious outputs when optical navigation is unable to determine the motion of the navigation device with sufficient accuracy. Whenever the optical navigation is inaccurate, then a synthetic navigation signal is used instead of the inaccurate optical navigation signal. 
     Two issues are addressed: detection of a situation in which optical navigation may not provide a correct motion estimation and the generation of a suitable synthetic output that replaces the output derived based on optical navigation. There are three basic elements. The first element is an Optical Navigator that estimates mouse motion based on a comparison of the current frame with a previous frame. The second element is a Synthetic Navigation Generator that produces an alternative navigation signal when the output of the Optical Navigator is deemed to be inaccurate. The third element is an Optical Quality Estimator that estimates the quality of the Optical Navigator output and determines whether the output of the Optical Navigator or the Synthetic Navigator should be used as the navigation device output. 
       FIG. 1  illustrates an optical navigation system  10  of the present invention. An Optical Quality Estimator  12  estimates the likelihood that the output of the Optical Navigator  14  is accurate. If so, then the optical navigator output is selected using control switch  17 . Otherwise, the control switch  17  selects the output of the Synthetic Navigation Generator. 
     In operation, the Optical Quality Estimator  12  can assess the expected quality of the Optical Navigator  14  output in several ways. An explanation of possible quality estimation techniques requires a more detailed understanding of the Optical Navigator  14 . 
       FIG. 2  illustrates the Optical Navigator  14  shown in  FIG. 1 . The Frame Memory  18  stores some or all of the digital values derived from a single frame observed by the Photosensor Array  20 . The Motion Likelihood Calculator  22  compares the current frame, obtained from the analog-to-digital converter (ADC)  24 , with a previous frame, that is stored in the Frame Memory  18  and assigns a likelihood value to each of various possible mouse movements. In an illustrative example, the maximum mouse movement between two frames is assumed to the diameter of one photosensor. The motion likelihood distribution, calculated by the Motion Likelihood Calculator  22 , includes values that correspond to “no movement” as well as for eight movements corresponding to movement of a single photosensor diameter in each of the eight compass directions: N, NE, E, SE, S, SW, W and NW. The values reflect the likelihood that a particular movement occurred. Typical methods of assessing likelihood are by comparing the two frames for all potential movements, using such methods as correlation or sum of squared differences. 
     From the motion likelihood distribution, the Maximum Likelihood Selector  26  determines which potential motion has the highest likelihood of occurring. This may be done by waiting until all potential movements have been assessed before finding the motion with highest likelihood, or maximum likelihood calculation could maintain the current maximum value and movement associated with the maximum value and compare each new value as it is generated. 
     While the Optical Navigator  14  in this example considers only nine possible motions, more sophisticated optical navigators can estimate motion reliably even if the optical navigation sensor moves several photosensor diameters from sample to sample. 
       FIGS. 3A–C  illustrate Optical Navigator  14  outputs from which the Optical Quality Estimator may receive data. In  FIG. 3A , the data comes from the Maximum Likelihood Selector  26 . In  FIG. 3B , the data comes from the Motion Likelihood Calculator  22 . In  FIG. 3C , the data comes from the Analog to Digital Converter  24 . 
     In  FIG. 3A , when the output of the Maximum Likelihood Selector  26  is incompatible with the movements that could be expected from an actual mouse, then it is likely that optical navigation quality is low. Mouse motion estimated by the Optical Navigator  14  is evaluated to determine whether the estimated motion is unlikely to occur during normal mouse operation, e.g. excessive velocity or acceleration. U.S. Pat. No. 6,433,780, “Seeing eye mouse for a computer system”, issued 13 Aug. 2002, assigned to Agilent Technologies, Gordon, et al. disclose one technique for determining when the velocity exceeds the expected mouse velocity. Gordon, et al. disclose selecting a synthetic navigation signal corresponding to “no movement” when estimated sensor velocity exceeds a threshold. 
     In  FIG. 3B , the output of the Motion Likelihood Calculator  22  is evaluated. One method disclosed by Gordon et al, in U.S. Pat. No. 6,433,780, looks for a predetermined “curvature”. Gordon teaches looking to see whether the likelihoods of multiple movements are too similar, i.e. there is a lack of a distinct maximum. Thus, if multiple motions are equally likely, the optical quality is likely to be low. In one illustrative example, the optical navigation quality is determined by checking if the maximum likelihood value differs from the other likelihood values by an amount that is statistically significant. 
     In  FIG. 3C , the digitized set of values from the Photosensor Array  20  is used by the Optical Quality Estimator  12 . When the frame lacks distinct features, it is difficult to navigate. The features may be counted to see if the number exceeds some threshold. Alternatively, a histogram of photosensor values of a frame shows a number of high and low values (i.e., they won&#39;t be uniform) when distinct features exist as will be described. 
     When few distinct features exist, the spread or “variance” of photosensor values of a frame drops due to lack of contrasting features. Therefore, by measuring the concentration of the histogram, optical navigation quality can be detected with a simple measurement that can be performed while monitoring the pixel values to ensure proper exposure. When optical navigation quality uses pixel values, the quality estimation depends on the data from a single frame and not on the relationship between two frames. 
     The histogram is more spread out when a frame has distinct features. This is expected since the automatic exposure system that controls the photosensor exposure time tries to adjust exposure time to maximize contrast. The histogram is more concentrated when the mouse is lifted for two reasons. One, the image becomes out of focus when lifted. Two, the center of the image spot moves off of the sensor imaging area, as the mouse is elevated. 
     In operation, the Optical Quality Estimator  12  receives a frame and generates a histogram. The histogram using N bins is computed by counting the number of pixels in each frame whose value lie in the bins. For example, when the pixels are digitized with q bits per pixel, then any pixel value lies between 0 and 2 q −1. This range is divided in N bins of equal width, e.g. 2 q /N. When q=8, the pixel values are between 0 and 255. If N=8, then each bin has a width of 256/8=32. Pseudocode 1 illustrates one way to compute the histogram. 
                                                                 for all pixels                bin − pixel value / 32 ; /* compute bin number */           histogram[bin] =histogram[bin] + 1 ;                end                        
Table 1 shows the histogram (count in each bin) for a typical frame when the mouse is on a surface and when lifted. The frame is assumed to have 8 bits per photosensor value.
 
     
       
         
               
               
               
               
               
               
               
               
               
             
           
               
                 TABLE 1 
               
               
                   
               
               
                   
                   
                   
                   
                 96– 
                 128– 
                 160– 
                 192– 
                 224– 
               
               
                 Bin Range 
                 0–31 
                 32–63 
                 64–95 
                 127 
                 159 
                 191 
                 223 
                 255 
               
               
                   
               
             
             
               
                 On surface 
                 16 
                  71 
                 103 
                 137 
                 121 
                 31 
                 5 
                 0 
               
               
                 Lifted 
                  7 
                 106 
                 302 
                  62 
                  7 
                  0 
                 0 
                 0 
               
               
                   
               
             
          
         
       
     
     Simple measures of concentration may be used to determine the optical navigation quality. One is the spread between the maximum and minimum occupied bins. A bin is considered occupied when the number of pixels whose values lie in it cannot be attributed to noise. Pseudocode 2 determines the minimum and maximum indices of occupied bins. In this embodiment, 8 bins are used, indexed 0 to 7. The counts in the histogram are assumed to be in the array called hist[ ] 
                                                                                                                                   minbin = 7; /* initial values */           maxbin = 0;           for (i = 7; i &gt;= 0; i−−)                if (hist[i] &gt; histogram_noise_level)                minbin = i;                /* find smallest index of occupied histogram bin */                for (i = 7; i &lt;= 0; i++)                if (hist[i] &gt; histogram_noise_level)                maxbin = i;                /* find largest index of occupied histogram bin */            /* If the spread is too small, optical navigation is poor */       spread = maxbin − minbin       if (spread &lt; MIN_SPREAD)                declare_poor_optical_navigation_quality( );                        
Other measures of spread that could be used include variance, third, fourth, or higher moments.
 
     Another way to evaluate optical navigation quality does not depend upon information from the optical navigator. For example, a switch may be included on the mouse housing such that the switch is depressed when the user lifts the mouse from the surface upon which the mouse usually operates. In this case, the Optical Quality Estimator  12  switches to the synthetic motion signal because the surface will be too distant for accurate optical navigation. Once the Optical Quality Estimator  12  determines that the optical navigation signal may not provide accurate information, it can switch to an alternate navigation signal. The Synthetic Navigation Generator  16  provides a synthetic motion signal that may be used instead. The desired behavior of the Synthetic Navigation Generator  16  depends on why the optical navigation quality is inadequate. When the mouse has been raised above the surface upon which navigation occurs, then the most appropriate navigation output may be to indicate that no motion is occurring. When the mouse moves over a spot on the surface for which optical navigation is difficult, then the most appropriate navigation output may be to continue the most recently observed optical navigation signals, with perhaps a running average of a plurality of the most recently observed optical navigation signals to provide more consistent mouse behavior. 
     In an alternate embodiment, the Optical Quality Estimator  12  may select from among multiple synthetic navigation signals. For example, perhaps different reasons for poor optical navigation can be distinguished by observing the digitized photosensor values at the output of the ADC  24 . 
     Another possibility is to construct a synthetic output that works adequately in multiple cases. For example, the initial output of the Synthetic Navigation Generator  16  may be to continue the most recently observed motion while the optical navigation data was good, and then to decrease the magnitude of the movement over time until the output signal indicates no movement. 
     The invention may be implemented in multiple ways. For example, the Optical Quality Estimator  12 , Synthetic Navigation Generator  16 , and the control switch all may be implemented as an integrated circuit. Alternatively, the various elements of the invention may be distributed in two or more places. For example, the Optical Navigator  14  and Optical Quality Estimator  12  may be located on an integrated circuit, and the Synthetic Navigation Generator  16  and control switch may be located in a separate microcontroller. 
     It is even possible that an element of the invention can be split into pieces, and the pieces implemented in different places. For example, consider an Optical Quality Estimator  12  that estimates quality based on the number of navigation features in a frame, where a feature is indicated by a pair of adjacent pixels that differ by a preselected amount. If the number of features is greater than a preselected value, then the optical navigation quality is considered acceptable. It is possible to implement the Optical Quality Estimator  12  in two pieces: the first piece is the navigation feature counter and the second piece is a comparator that determines whether the preselected threshold has been exceeded. The first piece can be included on the same integrated circuit as the Optical Navigator  14  and the second piece can be implemented on a separate microcontroller. 
     A consequence of splitting the Optical Quality Estimator  12  as described above is that the feature count information must be made available to the microcontroller (not shown). One way to do this is to make the current feature count available in a register of the chip that contains the Optical Navigator  14 . The microcontroller reads the value in the register, compares the read value to a preselected threshold, and selects the optical navigation signal or the synthetic navigation signal accordingly.