SLAM input options

Data to be processed for SLAM can be collected from a variety of devices.  The most common way is with stereoscopic cameras.  However Monovision SLAM has been getting more and more popular as of late.  Other mediums of SLAM include sonar, IR scanners, infrared range finders, even WIFI signals. In some cases, attempts have even been made to use multiple devices in order to compensate for the strengths and weaknesses of each other.  The issue with this is is compounds the complexity of writing an algorithm for such methods.  Allow me to go a bit more in depth about the benefits and weaknesses of input devices. First off ill knock off the weakest mediums from the list starting with sonar.  According to

U. Frese in his paper on SLAM "..sonar data is usually so bad, that it is very hard to derive any reliable landmark information or even identify a landmark from it.". IR scanners are cheap and good at detecting nearby obstacles, however have a short range and detecting obstacle shape.  Infrared rangefinders, while not having the problem with distance also have difficulty with shape recognition and also when scanning glass surfaces.  Overall vision sensors seem to be the best choice in most cases.G Zunino reinforces this opinion with a list of the benefits and downsides of visual sensors in his paper on SLAM.

•Large amount of information.

•Capability of getting 3D information about the environment.

•Cameras are passive sensors, they don’t have to emit sound or light pulses as sonar and laser sensors.

The drawbacks are:

•High computing requirement to extract the information from the images.

•Vision is highly influenced by the lighting.

•It is still expensive