I think most researches would laugh at the idea. Why bother trying IR when you could use a Lidar ? :Budget.
The Traxster is a rugged tracked robot from RoboticsConnection that often comes with a pan-tilt head containing three Sharp IR sensors that have a max range of 80cm.
The image on the left shows a 0.5cm per pixel map of the traxster panning its head and recording the distances from the three IRs and sending it to an occupancy grid using a cone model of width 0.1 radians and an obstacle depth of 2cm.
As none of the IRs are located above the center of rotation, there is a little work needed in order to place them correctly so that when they pan round, the three sets of data point to the right places in the map. Fortunately, I had all that code already, so it was just a question of saying distanceSensor[i].AttachToServo(x,y,theta). Where the x,y and theta are the offsets from the servo's center of rotation. You can see the space left by the head as it turns as a grey area within the robot's rectangle.
However, as with all tracked vehicles with skid steer, the odometry can be pretty misleading when skidding on the spot. The image on the left shows a 2cm per pixel map. The odometry path is shown as a blue line. You can see that odometry would have made the robot go through an obstacle, but once fed into grid slam, the estimated true position has diverged away from the odometry leading to a slightly better map.
The robot was controlled from within Microsoft Robotics Studio using a joystick, sending Pid velocity commands. Only by driving very slowly to avoid skidding, and panning the head continually was it possible to make a consistent map. It might be possible to correct rotational error by fusing the odometry with compass data, but even this is unlikely to work on large scale maps where the IRs often read max range. My next experiments will be with sonar.