I am researching on vehicle speed sensors. I was wondering if there are some technology or software algorithms that could be used to determine vehicle's its own speed without using GPS.
I have been drawn towards using radar or lidar sensors along with some computing algorithms to estimate moving vehicle's own speed. How possible is this?
Well, you can identify static objects and estimate "their speed" using the doppler effect.
Alternatively you can try to go by known distances, e.g. due to regulations, of objects you detect and then monitor them moving.
But the easiest way to get the own speed is either using the speed sensors and/ or the inertial measurement unit of the braking system.
Related
I'm in the process of buying a 7.5 acre plot of land in a wooded, hilly area. I would estimate that the elevation varies about 50 feet from the bottom of the creek to the top of the hill. I would like to find a good method for measuring the topography of the land so I can create a 3D model. It would be tremendously useful to be able to try out different land development ideas and to simulate locations for future buildings.
My low-tech version of doing this would be to set up a laser level and go around taking elevation measurements in a 3' or so grid pattern. As I thought about that, I realized that smartphones and similar devices have quite a few sensors built in that might make this a lot easier.
I learned about software that will use a drone to capture data and images to automatically generate a topo map and 3D model. Drone Deploy is one such tool. I do have a DJI Phantom 4, but I don't know if it's feasible to fly such an intricate path among trees to scan the entire property. I wonder if there's another way to use this amazing modern hardware (phone or drone) to make my task easy.
I would appreciate hearing any thoughts and ideas about this!
The thing with dronedeploy is that you fly above the trees usually 30meters is ok. In a cross pattern.
Why do you want to fly between the trees? You have to explain that first.
I was trying to find a way to calibrate a magnetometer attached to a vehicle as Figure 8 method of calibration is not really posible on vehicle.
Also removing magnetomer calibrating and fixing won't give exact results as fixing it back to vehicle introduces more hard iron distortion as it was calibrated without the vehicle environment.
My device also has a accelerometer and gps. Can I use accelerometer or gps data (this are calibrated) to automatically calibrate the magnetometer
Given that you are not happy with the results of off-vehicle calibration, I doubt that accelerometer and GPS data will help you a lot unless measured many times to average the noise (although technically it really depends on the precision of the sensors, so if you have 0.001% accelerometer you might get a very good data out of it and compensate inaccuracy of the GPS data).
From the question, I assume you want just a 2D data and you'll be using the Earth's magnetic field as a source (as otherwise, GPS wouldn't help). You might be better off renting a vehicle rotation stand for a day - it will have a steady well known angular velocity and you can record the magnetometer data for a long period of time (say for an hour, over 500 rotations or so) and then process it by averaging out any noise. Your vehicle will produce a different magnetic field while the engine is off, idle and running, so you might want to do three different experiments (or more, to deduce the engine RPM effect to the magnetic field it produces). Also, if the magnetometer is located close to the passengers, you will have additional influences from them and their devices. If rotation stand is not available (or not affordable), you can make a calibration experiment with the GPS (to use the accelerometers or not, will depend on their precision) as following:
find a large flat empty paved surface with no underground magnetic sources (walk around with your magnetometer to check) then put the
vehicle into a turn on this surface and fix the steering wheel use the cruise control to fix the speed
wait for couple of circles to ensure they are equal make a recording of 100 circles (or 500 to get better precision)
and then average the GPS noise out
You can do this on a different speed to get the engine magnetic field influence from it's RPM
I had performed a similar procedure to calibrate the optical sensor on the steering wheel to build the model of vehicle angular rotation from the steering wheel angle and current speed and that does not produce very accurate results due to the tire slipping differently on a different surface, but it should work okay for your problem.
We have an embedded device mounted in a vehicle. It has accelerometer, gyrosopce and GPS sensors on board. The goal is to distinguish when vehicle is moving forward and backward (in reverse gear). Sensor's axis are aligned with vehicle's axis.
Here's our observations:
It's not enough to check direction of acceleration, because going backwards and braking while moving forward would show results in the same direction.
We could say that if GPS speed decreased 70 -> 60 km/h it was a braking event. But it becomes tricky when speed is < 20 km/h. Decrease 20 -> 10 km/h is possible when going both directions.
We can't rely on GPS angle at low speeds.
How could we approach this problem? Any ideas, articles or researches would be helpful.
You are looking for Attitude and heading reference system implementation. Here's an open source code library. It works by fusing the two data sources (IMU and GPS) to determine the location and the heading.
AHRS provides you with roll, pitch and yaw which are the angles around X, Y and Z axises of the IMU device.
There are different algorithms to do that. Examples of AHRS algorithms are Madgwick and Mahony algorithms. They will provide you with quaternion and Eurler angles which can easily help you identify the orientation of the vehicle at any time.
This is a cool video of AHRS algo running in real time.
Similar question is here.
EDIT
Without Mag data, you won't get high accuracy and your drift will increase over time.
Still, you can perform AHRS on 6DoF (Acc XYZ and Gyr XYZ) using Madgwick algorithm. You can find an implementation here. If you want to dive into the theory of things, have a look at Madgwick's internal report.
Kalman Filter could be an option to merge your IMU 6DoF with GPS data which could dramatically reduce the drift over time. But that requires a good understanding of Kalman Filters and probably custom implementation.
I want to evaluate the performance of several SDKs / frameworks for depth cameras. These cameras can either be using Time-of-Flight or structured light.
The framework should be capable (at least) of person tracking / blob detection and gesture recognition.
So far I found the following frameworks:
OpenNI (structured light only)
Microsoft Kinect SDK (Kinect only)
Beckon SDK by Omek Interactive (ToF and structured light)
iisu by SoftKinetic (ToF and structured light)
Are there any other frameworks I should be aware of?
EDIT: I found this article by Techradar that seems to indicate that these are indeed the only options currently available.
Any feedback would be very much appreciated!
I have found some interesting links on this. You can take MIT's approach using CodAC . They list lots of facts on this post, the most important ones I will post here.
9. What are limitations of this technique?
The main limitation of our framework is inapplicability to scenes with curvilinear
objects, which would require extensions of the current mathematical model.
Another limitation is that a periodic light source creates a wrap-around error
as it does in other TOF devices. For scenes in which surfaces have high reflectance
or texture variations, availability of a traditional 2D image prior to our data
acquisition allows for improved depth map reconstruction as discussed in our paper.
10. What are advantages of this technique/device and how does it
compare with existing TOF-based range sensing techniques?
In laser scanning, spatial resolution is limited by the scanning time.
TOF cameras do not provide high spatial resolution because they rely on a
low-resolution 2D pixel array of range-sensing pixels. CoDAC is a single-sensor,
high spatial resolution depth camera which works by exploiting the sparsity of natural
scene structure.
11. What is the range resolution and spatial resolution of the CoDAC system?
We have demonstrated sub-centimeter range resolution in our experiments.
This is significantly better than fundamental limit of about 10 cm that would
arise from using a detector with 0.7 nanosecond rise time if we were not using
parametric signal modeling. The improvement in range resolution comes from the
parametric modeling and deconvolution in our framework. We refer the reader to
our publications for complete details and analysis.
We have demonstrated 64-by-64 pixel spatial resolution,
as this is the spatial resolution of our spatial light modulator.
Spatially patterning with a digital micromirror device (DMD) will enable
much higher spatial resolution. Our experiments use only 205 projection patterns,
which correspond to just 5% of number of pixels in the reconstructed depth map.
This is a significant improvement over raster scanning in LIDAR, and it is
obtained without the 2D sensor array used in TOF cameras.
Also another interesting project I found on Youtube uses libfreenect and libusb
There is also dSensingNI which is described as
This work presents an approach to overcome the disadvantages of existing interaction
frameworks and technologies for touch detection and object interaction. The robust and
easy to use framework dSensingNI (Depth Sensing Natural Interaction) is described,
which supports multitouch and tangible interaction with arbitrary objects. It uses
images from a depth-sensing camera and provides tracking of users fingers of palm of
hands and combines this with object interaction, such as grasping, grouping and
stacking, which can be used for advanced interaction techniques.
So you have hit most of them out there, especially that use Kinect, but there are a few other options out there! Hope this Helps!
In a soccer game, I am computing a steering force using steering behaviors. This part is ok.
However, I am looking for the best way to implement simple 2d human locomotion.
For instance, the players should not "steer" (or simply add acceleration computed from steering force) to its current velocity when the cos(angle) between the steering force and the current velocity or heading vectors is lower than 0.5 because it looks as if the player is a vehicule. A human, when there is an important change of direction, slows down and when it has slowed enough, it starts accelerating in the new direction.
Does anyone have any advice, ideas on how to achieve this behavior? Thanks in advance.
Make it change direction very quickly but without perfect friction. EG super mario
Edit: but feet should not slide - use procedural animation for feet
This is already researched and developed in an initiative called "Robocup". They have a simulation 2D league that should be really similar to what you are trying to accomplish.
Here's a link that should point you to the right direction:
http://wiki.robocup.org/wiki/Main_Page
Maybe you could compute the curvature. If the curvature value is to big, the speed slows down.
http://en.wikipedia.org/wiki/Curvature
At low speed a human can turn on a dime. At high speed only very slight turns require no slowing. The speed and radius of the turn are thus strongly correlated.
How much a human slows down when aiming toward a target is actually a judgment call, not an automatic computation. One human might come to almost a complete stop, turn sharply, and run directly toward the target. Another human might slow only a little and make a wide curving arc—even if this increases the total length to the target. The only caveat is that if the desired target is inside the radius of the curve at the current speed, the only reasonable path is to slow since it would take a wide loop far from the target in order to reach it (rather than circling it endlessly).
Here's how I would go about doing it. I apologize for the Imperial units if you prefer metric.
The fastest human ever recorded traveled just under 28 mph. Each of your human units should be given a personal top speed between 1 and 28 mph.
Create a 29-element table of the maximum acceleration and deceleration rates of a human traveling at each whole mph in a straight line. It doesn't have to be exact--just approximate accel and decel values for each value. Create fast, medium, slow versions of the 29-element table and assign each human to one of these tables. The table chosen may be mapped to the unit's top speed, so a unit with a max of 10mph would be a slow accelerator.
Create a 29-element table of the sharpest radius a human can turn at that mph (0-28).
Now, when animating each human unit, if you have target information and must choose an acceleration from that, the task is harder. If instead you just have a force vector, it is easier. Let's start with the force vector.
If the force vector's net acceleration and resultant angle would exceed the limit of the unit's ability, restrict the unit's new vector to the maximum angle allowed, and also decelerate the unit at its maximum rate for its current linear speed.
During the next clock tick, being slower, it will be able to turn more sharply.
If the force vector can be entirely accommodated, but the unit is traveling slower than its maximum speed for that curvature, apply the maximum acceleration the unit has at that speed.
I know the details are going to be quite difficult, but I think this is a good start.
For the pathing version where you have a target and need to choose a force to apply, the problem is a bit different, and even harder. I'm out of ideas for now--but suffice it to say that, given the example condition of the human already running away from the target at top stpeed, there will be a best-time path that is between on the one hand, slowing enough while turning to complete a perfect arc to the target, and on the other hand stopping completely, rotating completely and running straight to the target.