Difference between revisions of "Object Detection"
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| + | In order to successfully run the bike on the street, we need to navigate around obstacles. There are several parts to this objective, including classifying objects based on their movement and size, and creating algorithms to navigate around a type of obstacle. This paper by CMU provides background: http://www.cs.cmu.edu/~mmv/papers/05sbai-juan.pdf. | ||
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==Method to Classify Objects for Navigation== | ==Method to Classify Objects for Navigation== | ||
We classified objects based on their size and their movement. We are working on traffic signals separately. For size, there are two groups, one where the obstacle’s diameter is less than the width of the sidewalk combined with the width of the bike, and one where the obstacle prevents the bike from moving on that path. We are currently developing Python algorithms to navigate around objects in these two groups. We receive a Point32 message of type geometry_msgs from vision, and that contains x,y, and z coordinates for the obstacle, which we can use to find the diameter of the obstacle. Then, to detect moving objects, we are researching neural networks to allow the bike to learn how to navigate around different moving objects through simulation. Another possibility is to create algorithms for categories of moving objects, such as small animals, mid-sized animals, and pedestrians. | We classified objects based on their size and their movement. We are working on traffic signals separately. For size, there are two groups, one where the obstacle’s diameter is less than the width of the sidewalk combined with the width of the bike, and one where the obstacle prevents the bike from moving on that path. We are currently developing Python algorithms to navigate around objects in these two groups. We receive a Point32 message of type geometry_msgs from vision, and that contains x,y, and z coordinates for the obstacle, which we can use to find the diameter of the obstacle. Then, to detect moving objects, we are researching neural networks to allow the bike to learn how to navigate around different moving objects through simulation. Another possibility is to create algorithms for categories of moving objects, such as small animals, mid-sized animals, and pedestrians. | ||
Revision as of 23:40, 18 May 2020
In order to successfully run the bike on the street, we need to navigate around obstacles. There are several parts to this objective, including classifying objects based on their movement and size, and creating algorithms to navigate around a type of obstacle. This paper by CMU provides background: http://www.cs.cmu.edu/~mmv/papers/05sbai-juan.pdf.
We classified objects based on their size and their movement. We are working on traffic signals separately. For size, there are two groups, one where the obstacle’s diameter is less than the width of the sidewalk combined with the width of the bike, and one where the obstacle prevents the bike from moving on that path. We are currently developing Python algorithms to navigate around objects in these two groups. We receive a Point32 message of type geometry_msgs from vision, and that contains x,y, and z coordinates for the obstacle, which we can use to find the diameter of the obstacle. Then, to detect moving objects, we are researching neural networks to allow the bike to learn how to navigate around different moving objects through simulation. Another possibility is to create algorithms for categories of moving objects, such as small animals, mid-sized animals, and pedestrians.
