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Hi MarkyMark,

I have had success with this setup. I made a small differential-drive bot that mapped using sonar, wheel odometry, and inertial sensors. Some of the key parts that made it work: - Stagger your sonar sensor's phase, especially if the sensors are looking in the same direction (or opposite by 180degrees). Use time division on the sampling. This greatly reduces noise. - BUFFER YOUR SONAR DATA. This is extremely important! My implementation used two sonar sensors that swept side to side. To get it to work, I had to buffer it for ~0.5s (thus creating a "laserscan" of about 25 data points) - it will not work very well if you publish single- or two-point "laserscans" - Use a coarse occupancy gridmap. I had good results with only 2.5cm pixels and large. (Also, since I was doing this on an arduino, it was a great way to drop the floating point numbers and use raw bytes instead) - Your localization will suffer from the extra delay due to buffering the sonar. If your robot doesn't need to respond quickly, it's better to use a coordinate transform snapshotted during the middle of the buffering operation, not at the end.

So YES this was a success for my project. Slow localization wasn't an issue for the environment I was using the robot in. If you have an extra couple hundred dollars, I'd opt for a cheap laserscanner. The objective of my project was to do SLAM on as small a budget as possible.