I have a time-of-flight proximity sensor and I used 2 of the same sensor in order to get a bigger field-of-view by stacking them vertically. I measured from 0° to 20° using the first sensor and measured from 15° to 35° using the second sensor. The sensors return csv file with confidence value and also distance of the detected object for each pixel. What would be the method I could use to combine these data together?
Related
Given a dataset which consists of geographic coordinates and the corresponding timestamps for each record, I want to know if there's any suitable measure that can determine the closeness between two points by taking the spatial and temporal distance into consideration.
The approaches I've tried so far includes implementing a distance measure between the two coordinate values and calculating the time difference separately. But in this case, I'd require two threshold values for both the spatial and temporal distances to determine their overall proximity.
I wanted to know there's any single function that can take in these values as an input together and give a single measure of their correlation. Ultimately, I want to be able to use this measure to cluster similar records together.
I am working on gait data which has 3D coordinates of position for 15 joints per frame. I am trying to estimate velocity of ankle joint but I don't know how to find across frames. Given frame rate is 30 fps. Also I want to find angles between two segments let's say thigh and shank of same leg.
I have an problem I need some help with. I have 2 sensors (a weather sensor and a camera) mounted under a large balloon (basically). The weather sensor records pitch, roll, yaw, altitude, heading (0-360), lon and lat. I also have x,y,z values that represent the offset distance that the weather sensor is from the camera. The camera does not have it's own INS so the values from the weather sensor are sent to the camera. However, since those values are coming from the weather sensor that is not in the same position as the camera, the values are not accurate. I need to perform calculations on the values before sending them to the camera and that is where I need help.
For the record:
Both devices are facing the front of the craft
The X is the Front to Back axis
The Y is the Top to Bottom axis
The Z is the Left to Right axis
coordinate planes diagram
I know the formula to get coordinates for a target point given a starting point, bearing, and distance. I can get the distance by using the Pythagorean Theorem (using the measured X and Z values). Those are 10.58055" and 17.53322" respectively. We already have the starting point (it comes from the weather sensor).
First, am I on the right track here?
Second, how do I appropriately calculate bearing? I can use trig to get the angle that the weather sensor is offset from the camera, which I think is required. I also think I need to account for the orientation of the sensor to the camera (i.e. if the weather sensor is in front of the camera, it needs to "turn around" to get to the camera). This would mean that if the X value is negative and the Z value is positive, I would subtract my angle (let's call it theta) from 180. However, that would only work if I was heading north so I believe you then need to add in the heading (that came from the weather sensor).
I think I am close on this. I need some smarter people letting me know if I am approaching this correctly and then possibly little things like the appropriate way to handle the bearing measurement going above 360 (which I believe is to just subtract 360).
Below is a scatter chart that displays two data sets using excel. the blue data set are observed(actual) location coordinates of detected small cars on a world plane. The orange data set represent the same location coordinates, however reconstructed using inverse mapping process which consists of many processes like camera calibration, finding camera pose, etc.
So my question is
is there a way i can connect every blue point with its correspondent orange one in Excel ?
Thanks in Advance
Apologies for misunderstanding problem.
We will will start by creating two series, one for observed and another for reconstructed.
We then create a new series for each observed and reconstructed pair. Each of these series will have a line connecting them.
We then order the series such that the first two series we created, of the observed and reconstructed data sets, are brought to the front.
Naive Question:
In the attached snapshot, I am trying to figure out the correlation concept when applied to actual values and to calculation performed on those actual values and creating a new stream of data.
In the example,
Columns A,B,C,D,E have very different correlation but when I do a rolling sum on the same columns to get G,H,I,J,K the correlation is very much the same(negative or positive.
Are these to different types of correlation or am I missing out on something.
Thanks in advance!!
Yes, these are different correlations. It's similar to if you were to measure acceleration over time of 5 automobiles (your first piece of data) and correlate those accelerations. Each car accelerates at different rates over time leaving your correlation all over the place.
Your second set of data would be the velocity of each car at each point in time. Because each car is accelerating at a pretty constant rate (and doing so in two different directions from the starting point) you either get a big positive or big negative correlation.
It's not necessary that you get that big positive or big negative correlation in the second set, but since your data in each list is consistently positive or negative and grows at a consistent rate, it correlates with either similar lists.