Machine vision based obstacle avoidance system
First Claim
Patent Images
1. A mobile platform comprising:
- a base structure;
a drive mechanism coupled to the base structure;
a machine vision system including;
an optical input module configured to capture an image in the form of a signal;
a pre-processing module configured to;
receive the signal from the optical input module, convert the signal into a first digital image,set a first threshold comprising;
IL=max{i|f(i−
δ
)>
f(i), f(i+δ
)>
f(i)}set a second threshold comprising;
IR=min{j|f(j−
δ
)>
f(j), f(j+δ
)>
f(j)}wherein i and j are each an intensity, f(i) is a number of pixels having the intensity i found from an intensity histogram, and δ
is a resolution parameter;
invert the first digital image;
an image normalizing module configured to normalize the inverted digital image by;
setting a first plurality of pixels in the inverted digital image having an intensity less than the first threshold or greater than the second threshold to a first predetermined intensity, andsetting a second plurality of pixels in the inverted digital image having an intensity greater than or equal to the first threshold or less than or equal to the second threshold to a second predetermined intensity;
an edge detection module configured to;
receive the normalized digital image, and perform edge detection on the normalized digital image, thereby outputting a bounding graph indicating edges of detected objects in the image; and
a detection module configured to;
receive the bounding graph, determine local maximums and zeros in the bounding graph, and determine a dynamic mask based on the local maximums and zeros, wherein the dynamic mask is applied to a second digital image to determine the presence of an object.
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Abstract
Machine vision based obstacle avoidance system is provided. The system utilizes a CCD camera to capture an image. A normalized image and dynamic masking is used in object detection.
16 Citations
20 Claims
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1. A mobile platform comprising:
- a base structure;
a drive mechanism coupled to the base structure;
a machine vision system including;
an optical input module configured to capture an image in the form of a signal;a pre-processing module configured to;
receive the signal from the optical input module, convert the signal into a first digital image,set a first threshold comprising;
IL=max{i|f(i−
δ
)>
f(i), f(i+δ
)>
f(i)}set a second threshold comprising;
IR=min{j|f(j−
δ
)>
f(j), f(j+δ
)>
f(j)}wherein i and j are each an intensity, f(i) is a number of pixels having the intensity i found from an intensity histogram, and δ
is a resolution parameter;invert the first digital image; an image normalizing module configured to normalize the inverted digital image by;
setting a first plurality of pixels in the inverted digital image having an intensity less than the first threshold or greater than the second threshold to a first predetermined intensity, andsetting a second plurality of pixels in the inverted digital image having an intensity greater than or equal to the first threshold or less than or equal to the second threshold to a second predetermined intensity;
an edge detection module configured to;receive the normalized digital image, and perform edge detection on the normalized digital image, thereby outputting a bounding graph indicating edges of detected objects in the image; and
a detection module configured to;receive the bounding graph, determine local maximums and zeros in the bounding graph, and determine a dynamic mask based on the local maximums and zeros, wherein the dynamic mask is applied to a second digital image to determine the presence of an object. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
- a base structure;
-
11. A method of determining a presence of an object, comprising:
- inverting a digital image comprising a plurality of pixels;
setting a first threshold comprising;
IL=max{i|f(i−
δ
)>
f(i), f(i+δ
)>
f(i)}setting a second threshold comprising;
IR=min{j|f(j−
δ
)>
f(j), f(j+δ
)>
f(j)}wherein i and j are each an intensity, f(i) is a number of pixels having the intensity i found from an intensity histogram, and δ
is a resolution parameter;normalizing the inverted digital image by; setting first pixels in the plurality of pixels having an intensity value less than the first threshold or greater than the second threshold to a first predetermined intensity, and setting second pixels in the plurality of pixels having an intensity value between the first threshold and the second threshold to a second predetermined intensity; performing edge detection on the normalized digital image, thereby outputting a bounding graph indicating an edge of a detected object in the digital image; determining local maximums and zeros in the bounding graph;
determining a dynamic mask based on the local maximums and zeros; and
applying the dynamic mask to the normalized digital image to determine a presence of an object.- View Dependent Claims (12, 13, 14, 15, 16, 17, 18, 19)
- inverting a digital image comprising a plurality of pixels;
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20. A mobile platform comprising:
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a base structure comprising one of a motorized wheel chair, a wheel chair, a robotic cleaner, a robotic vacuum, and a cane; a drive mechanism coupled to the base structure; a machine vision system including;
an optical input module that captures an image in the form of a signal;a pre-processing module configured to;
receive the signal from the optical input module, convert the signal into a grayscale digital image, invert the digital image,set a first threshold comprising;
IL=max{i|f(i−
δ
)>
f(i), f(i+δ
)>
f(i)}set a second threshold comprising;
IR=min{j|f(j−
δ
)>
f(j), f(j+δ
)>
f(j)}wherein i and j are each an intensity, f(i) is a number of pixels having the intensity i found from an intensity histogram, and δ
is a resolution parameter;an image normalizing module configured to normalize the inverted digital image by;
setting a first plurality of pixels having an intensity less than the first threshold or greater than the second threshold to a first predetermined intensity, and setting a second plurality of pixels having an intensity greater than or equal to the first threshold or less than or equal to the second threshold to a second predetermined intensity;an edge detection module configured to;
receive the normalized digital image, and perform edge detection the normalized digital image, thereby outputting a bounding graph indicating edges of detected objects in the image;
an image filtering module configured to;
receive the bounding graph from the edge detection module, and filter noise peaks in the bounding graph;a clipping module configured to;
receive the filtered bounding graph from the image filtering module, remove a portion of the filtered bounding graph corresponding to additional noise in the filtered bounding graph, and replace the removed portion with a smoothed portion;a detection module configured to;
receive the clipped and filtered bounding graph, determine local maximums and zeros in the graph, and determine a dynamic mask based on the local maximums and zeros, wherein the dynamic mask is applied to a second digital image to determine the presence of an object;and an object detection module configured to;
partition the second digital image into a plurality of sections, and determine a location of the object by determining a section of the plurality of sections having the dynamic mask located therein.
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Specification