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Update 4/9/13

April 9, 2013

Here’s some details on recent events.

Last week we received a product donation from GoPro: a Hero 3 camera (Black Edition). Some of its notable features are the “ultra” wide angle lens, water-proof case, and attachable lenses. Unfortunately, there doesn’t seem to be much support for streaming pictures over USB, or much of anything for Linux. All we’ve been able to do so far is stream it to GoPro’s iPhone app and take pictures and video using a Micro SD card. Since the camera is set up to stream over HDMI, our current plan is to get an HDMI capture device and plug it into our PCI Express slot. We’re currently in the process of contacting companies that sell HDMI capture devices, since we’re pretty much out of money at this point.

We finished some initial work on getting the sonar array publishing into the system–a node has been created to act as a driver for an Arduino Omega board that is connected to the 12 sonars. I’m not sure if I’ve explained our reason for using a sonar array explicitly. We conceived of the idea originally when the Hokuyo started to malfunction, so we already had a plan to execute when it finally died. Inspired by what RAS did for IGVC in 2009, we have connected a bunch of sonar sensors together in a half-ring. There were about $3 each, but are actually supposed to be pretty good. The data isn’t scaled properly at the moment, so we haven’t been able to bag anything to analyze yet. Here’s a picture of the sonar array taken with the GoPro:


The VN 200 does not have support for OmniSTAR’s subscription service. The GPS data we’re getting back from it is accurate to within 5 meters when staying still, and about 2 meters when moving, but this will probably not be good enough. The competition requires that we reach waypoints within 2 meters. So, we’ve begun to contact different companies that produce GPS receivers that are explicitly compatible with OmniSTAR to see if we can get another product donation.

We’ve also been able to analyze the results of using messages from the VN 200 Inertial Navigation System (INS) in our Extended Kalman Filter (EKF). Unfortunately, from looking at some data we recorded the other day at the intramural fields, our EKF’s orientation estimates are better when ignoring these INS messages and instead working directly with the accelerometer and magnetometer messages (even with proper covariances for each). The INS yaw value appears to drift over time, whereas from using our calculations of roll, pitch, and yaw (using this as a reference), we do not observe any drift. We have not yet done any hard & soft iron calibration.

This weekend we hope to be able to go back to the intramural fields to see if we can autonomously navigate to GPS waypoints using both the sonar array and the camera. This would be a big milestone for us, since all we’ve done so far is navigate to local waypoints around obstacles using only the encoders (feedback from the wheels) as input to the EKF for localization. We haven’t ever navigated to actual GPS waypoints before, and we haven’t done anything autonomously while incorporating GPS and IMU sensors. Using the Hokuyo, the robot was able to navigate around obstacles very well; the same code run with the camera has proved decent, but it still scrapes the edges of orange obstacles. Integration of the sonar array scans and the camera image scans remains as yet untested and there’s still some work that needs to be done for it to work. So, it would be a huge step forward if we’re able to observe robust navigation with all these different components running at once.

Here is a dataflow diagram with the nodes that are currently running in the system:


click to view data flow



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