Embedded Vision Summit, May, 2016 – The original title of Larry Matthies, Senior Scientist from NASA Jet Propulsion Lab, was “Using Vision to Enable Autonomous Land, Sea and Air Vehicles”. Matthies added “the space” to emphasize the surroundings of these vehicles. “How I could forget about space”, he noted. When we think about autonomous vehicles, what comes to our minds are the vehicles on the roads but the scientists are continuing to develop the driverless vehicles for sea, air and space.

Matthies mentioned that the primary application domains and main JPL themes for autonomous vehicles are now as follows: 1. Land – all-terrain autonomous mobility; mobile manipulation 2. Sea: USV escort teams: UUVs for subsurface oceanography 3. Space: assembling large structures in Earth orbit 4. Air: Mars precision landing; rotorcraft for Mars and Titan; drone autonomy on Earth.

Then he described some of the capabilities and challenges that the scientists are facing. One important capability is absolute and relative localization.  The key challenges in this domain are: appearance variability, lighting, weather, seasonality, moving objects, and fail-safe performance. Localization has been tested on wheeled, tracked, and legged vehicles in both indoor and outdoor settings, as well as with drones, and Mars rovers and landers.

Another capability the speaker discussed is obstacle detection. This capability includes stationary or moving objects, obstacle type identification and classification, and the ability to determine the capacity and feasibility of terrain traffic. Complementing the detection functions are the understanding of other scene semantics as such landmarks signs, destinations, etc., perceiving people and their activities, and perception for grasping.

The challenges facing the observability sensors are non-trivial. Some of the difficult image characteristics include fast motion, variable lighting conditions such as low light, no light, and very wide dynamic range. The environment can have atmospheric conditions such as haze, fog, smoke or precipitation, and can have many difficult object parameters like featureless, specular, and transparent and terrain types such as obstacles in grass, water, snow, ice, mud. Finally, the last challenge that developers must address is the tradeoff between computational costs versus processor power.