# Arduino Quadcopter 2: State Estimation with Kalman Filtering of GPS Data Along with 3 other robotics students at Georgia Tech, I made a further development on my work on the Arduino Quadcopter GPS functions that I first introduced in this post. As shown in the introductory post, GPS data from the DJI Naza GPS receiver can be sent to Arduino via serial communications. The next step is to program Arduino to produce a location estimate based on those GPS data. Although raw (latitude,longitude) data could be used as a location estimate, problems arise when the quadrotor enters areas where the GPS signal strength is lower and (latitude, longitude) updates are received at a much lower frequency. In the outdoor tests we performed in Atlanta, the rate of GPS data retrieval varied from as high 2 Hz to as low as < 0.1 Hz. This means that if the quadrotor depends only on raw GPS data, its location estimate could be 10 seconds old or more. In addition to issues with data retrieval frequency, raw GPS data are also noisy, thus increasing the unreliability of such an unfiltered location estimation scheme.

The problems discussed above are what motivate the implementation of a Kalman Filter on board the Arduino control hardware. The Kalman Filter is a popular mathematical technique in robotics because it produces state estimates based on noisy sensor data. This great tutorial explains the Kalman Filter. You can find our online and offline Arduino implementations of the Kalman Filter on my github page. The most useful implementation is Arduino_Kalman_Online_With_Interpolation.ino because it updates the quadrotor’s state estimate in spite of a lack of GPS data from the receiver hardware.

Below is a poster we put together to succinctly describe the work. The figures show the valuable benefit of having state estimates even when GPS data are not available. Black marks show GPS data while pink marks show Kalman Filtered state estimates made by Arduino in real time. The beneficial smoothing effect and the 1.75m location estimation accuracy are also evident. # Arduino Quadcopter 2: GPS Integration With this arrangement, the Naza flight controller can use GPS to hold position given non-zero flight commands, and the Arduino can use GPS to send non-zero flight commands that direct the quadcopter to follow a GPS waypoint. The forum user pawlesky wrote an excellent tutorial and C++ library that makes it relatively easy for Arduino to interface with the Naza GPS device. My integration of pawlesky’s library into the Arduino code in the Receiver10 file is here; version 10 is still very experimental. Below is an image of the GPS data reported by the Arduino Mega that is running the new receiver code. Note how the heading estimate from the magnetometer updates much more frequently than the latitude and longitude estimates. With access to GPS data, the Arduino Quadcopter can be programmed to fully autonomously follow waypoints, which is a huge step forward.  `elevator.writeMicroseconds(1600);`