Vision for mobile robot navigation Jannes Eindhoven

Contents Introduction [2] Indoor navigation  Map based approaches [5]  Map building [1]  Mapless navigation [2] Outdoor navigation  In structured environments [3]  In unstructured environments [1] Summary [1]

Introduction Guilherme DeSouza Avinash Kak

Introduction[2] Summary of the developments of the last 2 decades. February 2002, thus not including latest developments Not all-comprising Gives examples of achievements

Indoor navigation – map based Acquire sensory information Detect landmarks Establish matches between observation and expectation Calculate position

Map based – absolute localization Initial position is unknown Multi belief system Known landmarks from a map Calculate the position, incorporating the uncertainty in the landmark locations Metric map

Map based – incremental localization Start position is known Uncertainty in position is projected in camera image Only use features in their expected image parts The position gets updated

Map based – incremental localization [2]

Map based – Landmark tracking Artificial landmarks Natural landmarks Geometric and even topological representations Example: NEURO- NAV

Map building Slow process Additional problem to localization Generating occupancy grid or topological map with metric representation at nodes

Mapless navigation No explicit map Storing instructions as direct association with perception

Mapless navigation – optical flow Corridor following Viewing sideways, measuring surface speed and proximity of both walls Direction determined by PID controller Problems with walls with little visible features

Mapless navigation - Appearance-based matching Memorizing the environment Associate commands or controls with these images Like a train with a movie as “track” Can be simplified by matching only vertical edges

Outdoor navigation Changing lightning is challenging Main application is car automation

Outdoor navigation – Structured environments Navlab's ALVINN Neural network with picture or Hough transformed picture as input Lighting and shadows are a problem

Outdoor navigation – Structured environments [2] Virtual camera images, extracted from the original camera image Red and blue contrasts Speed is required for automotive applications Hue / intensity images

Outdoor navigation - Unstructured Measuring local environment metrical Example: Pathfinder rover and lander

Conclusions In controlled environments a lot can be achieved with current knowledge In free or unpredictable environments, there is still a long way to go