Here is the image again (hopefully it will show up correctly this time).
The map created from row data is useless because all the noise in sensor measurement and movement errors. One way to fix this is by applying Bayesian reasoning to update the map using probabilistic rule. The Bayesian update rule I used is describe in the book "An Introduction to AI Robotics" by Murphy.
See image below for occupancy grid map updated by Bayesian rule (rendered in 3D):http://www.freeimagehosting.net/8bb31
The image shows the same set data processed by Bayesian update rule. The black line shows the route the robot took. Again each grid represent a 3cm by 3cm square, and the height of each grid shows the probability of that grid is being occupied. The darker color a gird is the more likely the grid is being occupied.
The map created by Bayesian update rule looks much better and is useable for path planning purposes.