-
# Context
## Available sensor data
* encoder at the motor
* IMU (3-axis accelerations, 3-axis gyro)
-> linear velocity and angular velocity after Kalman filtering
## Available environment dat…
-
Currently, I either repeat a package or insert silence.
Several better algorithms exists, list of papers:
* https://arxiv.org/pdf/2007.07132.pdf Machine Learning
* https://openhsu.ub.hsu-h…
-
Many examples as well as tests start by defining a linear dynamical system network followed by some effect that you want to demonstrate, eg demonstrate or test drawing the network, or demo/test teh Te…
ghost updated
11 years ago
-
It influences the optical flow with 'false' information, this needs to be counteracted.
A very promising approach is found in `B. Depth Estimation in Straight Flight`:
@inproceedings{zingg2010mav,
…
-
An effective software filtering technique can be employed by creating a dead zone / ignore zone X uS after a main pulse has fired. This would help mask noisy ringing typically exhibited after an ignit…
-
Save bandwidth:
"don't transmit to server if any of the sensor values haven't changed compared to the last transmitted values"
or make reaction time faster with
"if unexpected sensor value ch…
-
Hi there, I would like to ask if it is possible to convert this Kalman Filter code for positioning. Instead of using IMU values, I want to make x,y coordinate the input.
-
I saw Armadillo C++ was used in "figure_fitting.h" to solved a liner regression. Is this the only place the library is need?
-
How do you train other types of Mars dataset?
-
Now that I've created the little sensor demo that visualises the sensor output it's clear more work needs to be done on processing the magnetometer.
From what I've read so far and understand so far…