SciTech Chronicles. . . . . . . . .Mar 5th, 2025
#cornea #epithelial #transplantation #18-months #sunspot #earthquake #Prediction #tectonic #hydrosilylation #platinum #silicon #ligand #AI #entanglement #communication #photons #LiDAR #Autonomous #Farming #odometry
What's on my mind?
Disappointment and Disillusionment!
- Invested 7 yrs in #GoPiGo3 #robot
- Created #ROS2 nodes for #proprioception, #odometry, #LIDAR, #docking, #tts #mapping, #navigation, #simulation, #ObjectRecognition, and #LifeLogging
- My robot cannot safely and reliably navigate in my complex home environment.
- No slower turns, no inflation value can fix
I'm done, destroyed, no strength left for "one more try". GoPi5Go-Dave is on the shelf with other "reached its limit" robots.
At this reduced transfer rate, the #GoPiGo3 can make a two byte #SPI transfer in 0.0003 seconds, which means it can retrieve the left and right wheel encoder values roughly 1700 times per second - plenty fast enough for my #ros2humble robot to publish #wheelencoders and the resultant #odometry at 30 Hz.
#SLAM_Toolbox Mapping w/o Localization
Got slam_toolbox mapping with localization turned off, but managed to figure out how to serve up a better looking map from my house floor plan.
The weakness of dead reckoning #odometry becomes very evident with the floorplan map.
Next up - #DeadReckoning #navigation with #ros2humble #nav2 and #map_server
#Create3 #IRsensor SLAM - First Attempt
My ir2scan node is working well, but setting the 52 #slam_toolbox params is a mystery.
My create3_navigation slam.launch.py (using #Turtlebot4 LIDAR parms modified for Create3 IR "distance" sensor scan), with Wali facing a wall, maps a wall!
(bouncing around in angle in front of bot...)
As I proceed to drive Wali around the room, more walls enclosing a "known to be open" space are mapped, but not where the sensors and #odometry put them.
Video De-shaker Software Measures Linear Rail Quality
Here's an interesting experiment that attempts to measure the quality of a linear rail by using a form of visual odometry, accomplished by mounting a camera on the rail and analyzing the video with open-source software usually used to stabilize shaky video footage. No linear rail is perfect, and it should be possible to measure the degree of imperfection by recording video footage while the camera moves down the length of the rail, and analyzing the result. Imperfections in the rail should cause the video to sway a proportional amount, which would allow one to characterize the rail's quality.
To test this idea, [Saulius] attached a high-definition camera to a linear rail, pointed the camera towards a high-contrast textured pattern (making the resulting video easier to analyze), and recorded video while moving the camera across the rail at a fixed speed. The resulting video gets fed into the Deshaker plugin for VirtualDub, of which the important part is the deshaker.log file, which contains X, Y, rotate, and zoom correction values required to stabilize the video. [Saulius] used these values to create a graph characterizing the linear rail's quality.
It's a clever proof of concept, especially in how it uses no special tools and leverages a video stabilizing algorithm in an unusual way. However, the results aren't exactly easy to turn into concrete, real-world measurements. Turning image results into micrometers is a matter of counting pixels, and for this task video stabilizing is an imperfect tool, since the algorithm prioritizes visual results instead of absolute measurements. Still, it's an interesting experiment, and perfectly capable of measuring rail quality in a relative sense. Can't help but be a bit curious about how it would profile something like these cardboard CNC modules.
#cnchacks #videohacks #cnc #linearrail #odometry #stabilization #video #virtualdub
