I am running mrpt_ekf_slam_2d (http://wiki.ros.org/mrpt_ekf_slam_2d) on an indoor robot equipped with an RPLidar. Landmarks are fed to the ekf by running a clustering algorithm on the lidar pointcloud.
I am having a problem that the ekf does not seem to associate observations with landmarks located behind the robot, that is with a yaw angle outside the interval [90, -90]. Those observation are simply just added as new landmarks.
Landmarks are mapped and associated correctly when in front of the robot, but as soon as they end up behind it, the ekf spams out multiple landmarks: 1
Here is the result of the same run when filtering the lidar scan to only include points within [90,-90]: 2
Has anyone encountered a similar problem using mrpt?
Interesting! It looks like a bug in mrpt-slam... please, could you file a bug report here for us to investigate it?
A example dataset / test case would be great.
Related
I'm using python 3.7 to process data from a set of ultrasonic sensors (HC-SR04) and an encoder placed on a moving robot. There are 8 ultrasonic sensors which are located on all four corners of the robot, facing front, sideways and backwards, and these give me the distance from their location to any nearby walls or lack thereof. The encoder is placed on a wheel and gives the distance travelled at the instant of sampling.
The objective here is to use the data from these sensors to map the path taken by the robot through a labyrinth and thus create the map for the labyrinth. And that's where I'm stuck, I've already ran the data through a filter to clean up the noise and I've managed to visually determine the path taken by the robot, i.e. if it's driving towards a wall, if it's turned left or right, but having upwards of four thousands data entries this is not ideal.
I'm looking for guidance or ideas on how to determine wether the robot is moving North, South, East or West and also how to create an image to display the path taken and furthermore the map of the labyrinth.
Any help will be greatly appreciated!
Edit: I'm adding graphs from the first 120 data points 1,2,3,4, as well as the placement of the sensors5
I am an undergraduate student working with detecting defects on a surface of an object, in a given digital image using image processing technique. I am planning on using OpenCV library to get image processing functions. Currently I am trying to decide on which defect detection algorithm to use, in order to detect defects. This is one of my very first projects related to this field, so it will be appreciated if I can get some help related to this issue. The reference image with a defect (missing teeth in the gear), which I am currently working with is uploaded as a link below ("defective gear image").
defective gear image
Get the convex hull of a gear (which is a polygon) and shrink is slightly so that it crosses the teeth. Make sure that the centroid of the gear is the fixed point.
Then sample the pixels along the hull, preferably using equidistant points (divide the perimeter by a multiple of the number of teeth). The unwrapped profile will be a dashed line, with missing dashes corresponding to missing teeth, and the problem is reduced to 1D.
You can also try a polar unwarping, making the outline straight, but you will need an accurate location of the center.
I would like to calculate the distance between my camera and a recognized "object".
The recognized "object" is a black rectangle sticker on a white board for example. I know the values of the rectangle (x,y).
Is there a method that I can use to calculate the distance with the values of my original rectangle, and the values of the picture of the rectangle I took with the camera?
I searched the forum for answeres, but none of the were specified to calculate the distance with these attributes.
I am working on a robot called Nao from Aldebaran Robotics, I am planing to use OpenCV to recognize the black rectangle.
If you could compute the angle taken up by the image of the target, then the distance to the target should be proportional to cot (i.e. 1/tan) of that angle. You should find that the number of pixels in the image corresponded roughly to the angles, but I doubt it is completely linear, especially up close.
The behaviour of your camera lens is likely to affect this measurement, so it will depend on your exact setup.
Why not measure the size of the target at several distances, and plot a scatter graph? You could then fit a curve to the data to get a size->distance function for your particular system. If your camera is close to an "ideal" camera, then you should find this graph looks like cot, and you should be able to find your values of a and b to match dist = a * cot (b * width).
If you try this experiment, why not post the answers here, for others to benefit from?
[Edit: a note about 'ideal' cameras]
For a camera image to look 'realistic' to us, the image should approximate projection onto a plane held infront of the eye (because camera images are viewed by us by holding a planar image in front of our eyes). Imagine holding a sheet of tracing paper up in front of your eye, and sketching the objects silhouette on that paper. The second diagram on this page shows sort of what I mean. You might describe a camera which achieves this as an "ideal" camera.
Of course, in real life, cameras don't work via tracing paper, but with lenses. Very complicated lenses. Have a look at the lens diagram on this page. For various reasons which you could spend a lifetime studying, it is very tricky to create a lens which works exactly like the tracing paper example would work under all conditions. Start with this wiki page and read on if you want to know more.
So you are unlikely to be able to compute an exact relationship between pixel length and distance: you should measure it and fit a curve.
It is a big topic. If you want to proceed from a single image, take a look at this old paper by A. Criminisi. For an in-depth view, read his Ph.D. thesis. Then start playing with the OpenCV routines in the "projective geometry" sectiop.
I have been working on Image/Object Recognition as well. I just released a python programmed android app (ported to android) that recognizes objects, people, cars, books, logos, trees, flowers... anything:) It also shows it's thought process as it "thinks" :)
I've put it out as a test for 99 cents on google play.
Here's the link if you're interested, there's also a video of it in action:
https://play.google.com/store/apps/details?id=com.davecote.androideyes
Enjoy!
:)
I will be start working on a robotics project which involves a mobile robot that has mounted 2 cameras (1.3 MP) fixed at a distance of 0.5m in between.I also have a few ultrasonic sensors, but they have only a 10 metter range and my enviroment is rather large (as an example, take a large warehouse with many pillars, boxes, walls .etc) .My main task is to identify obstacles and also find a roughly "best" route that the robot must take in order to navigate in a "rough" enviroment (the ground floor is not smooth at all). All the image processing is not made on the robot, but on a computer with NVIDIA GT425 2Gb Ram.
My questions are :
Should I mount the cameras on a rotative suport, so that they take pictures on a wider angle?
It is posible creating a reasonable 3D reconstruction based on only 2 views at such a small distance in between? If so, to what degree I can use this for obstacle avoidance and a best route construction?
If a roughly accurate 3D representation of the enviroment can be made, how can it be used as creating a map of the enviroment? (Consider the following example: the robot must sweep an fairly large area and it would be energy efficient if it would not go through the same place (or course) twice;however when a 3D reconstruction is made from one direction, how can it tell if it has already been there if it comes from the opposite direction )
I have found this response on a similar question , but I am still concerned with the accuracy of 3D reconstruction (for example a couple of boxes situated at 100m considering the small resolution and distance between the cameras).
I am just starting gathering information for this project, so if you haved worked on something similar please give me some guidelines (and some links:D) on how should I approach this specific task.
Thanks in advance,
Tamash
If you want to do obstacle avoidance, it is probably easiest to use the ultrasonic sensors. If the robot is moving at speeds suitable for a human environment then their range of 10m gives you ample time to stop the robot. Keep in mind that no system will guarantee that you don't accidentally hit something.
(2) It is posible creating a reasonable 3D reconstruction based on only 2 views at such a small distance in between? If so, to what degree I can use this for obstacle avoidance and a best route construction?
Yes, this is possible. Have a look at ROS and their vSLAM. http://www.ros.org/wiki/vslam and http://www.ros.org/wiki/slam_gmapping would be two of many possible resources.
however when a 3D reconstruction is made from one direction, how can it tell if it has already been there if it comes from the opposite direction
Well, you are trying to find your position given a measurement and a map. That should be possible, and it wouldn't matter from which direction the map was created. However, there is the loop closure problem. Because you are creating a 3D map at the same time as you are trying to find your way around, you don't know whether you are at a new place or at a place you have seen before.
CONCLUSION
This is a difficult task!
Actually, it's more than one. First you have simple obstacle avoidance (i.e. Don't drive into things.). Then you want to do simultaneous localisation and mapping (SLAM, read Wikipedia on that) and finally you want to do path planning (i.e. sweeping the floor without covering area twice).
I hope that helps?
I'd say no if you mean each eye rotating independently. You won't get the accuracy you need to do the stereo correspondence and make calibration a nightmare. But if you want the whole "head" of the robot to pivot, then that may be doable. But you should have some good encoders on the joints.
If you use ROS, there are some tools which help you turn the two stereo images into a 3d point cloud. http://www.ros.org/wiki/stereo_image_proc. There is a tradeoff between your baseline (the distance between the cameras) and your resolution at different ranges. large baseline = greater resolution at large distances, but it also has a large minimum distance. I don't think i would expect more than a few centimeters of accuracy from a static stereo rig. and this accuracy only gets worse when you compound there robot's location uncertainty.
2.5. for mapping and obstacle avoidance the first thing i would try to do is segment out the ground plane. the ground plane goes to mapping, and everything above is an obstacle. check out PCL for some point cloud operating functions: http://pointclouds.org/
if you can't simply put a planar laser on the robot like a SICK or Hokuyo, then i might try to convert the 3d point cloud into a pseudo-laser-scan then use some off the shelf SLAM instead of trying to do visual slam. i think you'll have better results.
Other thoughts:
now that the Microsoft Kinect has been released, it is usually easier (and cheaper) to simply use that to get a 3d point cloud instead of doing actual stereo.
This project sounds a lot like the DARPA LAGR program. (learning applied to ground robots). That program is over, but you may be able to track down papers published from it.
I'm currently studying for an exam (which is in two days) for computer vision but am getting pretty confused with the epipolar geometry stuff. I'm going through some past exam papers and I'm stuck on these two questions. Any help/explanations would be appreciated.
1) How are two cameras placed one with respect to another if epipoles in both the images coincide with the principle points (traces of optical axes)?
2) How are two cameras placed one with respect to another if epipoles in both the images seat infinitely far along the Y-axis of the world co-ordinate frame and have the same x-coordinate?
For the second one, my first guess was the cameras sit on top of one another and face the same direction. I'm not sure if it's right though.
The epipole is the position of one camera's centre of projection from the point of view of the other. So:
1) The two cameras are directly facing each other. Think of taking a photo, taking a few steps forwards, turning around 180 degrees and taking another picture. The line joining the centre of projections in the two images is normal to both the image planes.
2) Think of taking a photo, crouching down a few inches and taking another one. If the epipole is at infinity it means that the line joining the centre of projections of the two viewpoints is parallel to the image plane. (The other camera can still be rotated relative to the current viewpoint though)