Vision for mobile robot navigation

Jannes Eindhoven

2-3-2010

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