Vision for Mobile Robot Navigation: A Survey
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1 Vision for Mobile Robot Navigation: A Survey (February 2002) Guilherme N. DeSouza & Avinash C. Kak presentation by: Job Zondag 27 February 2009
2 Outline: Types of Navigation Absolute localization (Structured) Map-Based Indoor Map-Building Mapless (Unstructured) Navigation Outdoor Structured Unstructured Incremental localization Landmark tracking Optic flow Appearance based Object recognition
3 Outline: Types of Navigation Absolute localization (Structured) Map-Based Indoor Map-Building Mapless (Unstructured) Navigation Outdoor Structured Unstructured Incremental localization Landmark tracking Optic flow Appearance based Object recognition
4 Indoor Navigation: Map-Based Navigation Vision system needs incorporation of some knowledge of what the robot is supposed to see CAD (geometrical maps) occupancy maps VFF: Virtual Force Fields topological maps sequences of images Vision based localization steps: Acquire sensory information Detect landmarks Establish matches between observation and expectation Calculate position
5 Indoor Navigation: Map-Based Navigation Absolute or global localization: Robot's initial pose is unknown. Incremental localization: Robot's initial pose is proximately known. Goal is to refine the location coordinates. Landmark tracking: Keep track of landmarks in the consecutive images that are recorded as the robot moves.
6 Absolute or Global Localization Atiya and Hager (1993)
7 Incremental Localization: Geometrical Representation of Space Initial position known proximately Keep updating the (uncertainties in the) position of the robot FINALE Kosaka & Kak (1992) Geometrical representation of space Statistical model of uncertainty in the location of the robot (Gaussian distribution)
8 Incremental Localization: Geometrical Representation of Space Using Geometrical Representation of Space Propagation of Positional Uncertainty trough Commanded Motions
9 Incremental Localization: Geometrical Representation of Space Projecting Robot's Positional Uncertainty into Camera Image Kalman Filtering
10 Incremental Localization: Topological Representation of Space NEURO-NAV Meng & Kak (1992) Graph representation of the layout of the hallway 2 modules (using neural networks) Hallway Follower Landmark Detector Supervisory RuleBased Controller
11 Incremental Localization: Topological Representation of Space
12 Incremental Localization: Topological Representation of Space Corridor-following: Neural Networks trained using backpropagation when a Human Supervisior module takes control of the navigation Results (1993): 86 % correct steering 10 % incorrect steering 4 % no decision FUZZY-NAV Kak et al. (1995)
13 Landmark Tracking Possible when known: Approximate location of the robot Identity of the landmarks Landmarks Artificial (circles, barcodes, tape) Natural (doors, windows, trees etc.) Most often: template matching
14 Outline: Types of Navigation Absolute localization (Structured) Map-Based Indoor Map-Building Mapless (Unstructured) Navigation Outdoor Structured Unstructured Incremental localization Landmark tracking Optic flow Appearance based Object recognition
15 Map-Building Model of the world not always easy to generate First attempt: Moravec (1981) Stanford Cart World representation: 3D features plotted in a grid of 2 m2 cells 20 meters in 5 hours Moravec & Elfes (1985): occupancy grid
16 Map-Building Occupancy-grid-based approaches: cells with a probability of being occupied
17 Map-Building Occupancy-grid-based approaches: cells with a probability of being occupied Rich in geometrical detail Reliability depends on accuracy of the robot's odometry and sensor uncertainties Not computationally efficient for large or complex spaces Topological approaches Difficult to recognize previously visited nodes
18 Outline: Types of Navigation Absolute localization (Structured) Map-Based Indoor Map-Building Mapless (Unstructured) Navigation Outdoor Structured Unstructured Incremental localization Landmark tracking Optic flow Appearance based Object recognition
19 Mapless Navigation: Optical Flow
20 Mapless Navigation: Optical Flow Santos-Victor et al. (1993) Robot: Robee Mimics visual behavior of bees: centering reflex (when flying trough hallway) Lateral position of the eyes: Motion derived features in stead of depth information
21 Mapless Navigation: Optical Flow Sustained behavior: it is desirable that when the robot runs into a section of the corridor deficit in wall texture, the robot drives on.
22 Mapless Navigation: Appearance-Based Matching Store images or templates of the environment and associate those images with commands or controls that will lead the robot to its final destination Gaussier et al. (1997) Neural networks: map perception to action 270 degree image of the environment Local views (subwindows) at x-positions of maximum intensity values
23 Mapless Navigation: Appearance-Based Matching Gaussier et al. (1997) Local views define a place in the environment Each place is asociated with a direction (azimuth) towards the goal A neural network learns to associate views/place with direction
24 Mapless Navigation: Appearance-Based Matching Ohno et al. (1996) VSSR: View-Sequenced Route Representation Correlate video input with database images to determine the position of the robot Use dispacement between the view and template image to compute real world dispacement and required steering actions
25 Mapless Navigation: Object Recognition Kim & Nevatia (1995) Symbolic navigation approach E.g. go to the desk in front of you Establish landmarks from command S-map: squeezed 3D into 2D space map GPS-like path planner
26 Outline: Types of Navigation Absolute localization (Structured) Map-Based Indoor Map-Building Mapless (Unstructured) Navigation Outdoor Structured Unstructured Incremental localization Landmark tracking Optic flow Appearance based Object recognition
27 Outdoor Navigation Comparable to indoor navigation: Obstacle-avoidance, landmark detection, map building/updating, position estimation Normally no a priori map of the environment Structured: e.g. Road-following Unstructured: outdoor environment with no regular properties. e.g. Planetary terrain navigation Illumination
28 Outline: Types of Navigation Absolute localization (Structured) Map-Based Indoor Map-Building Mapless (Unstructured) Navigation Outdoor Structured Unstructured Incremental localization Landmark tracking Optic flow Appearance based Object recognition
29 Outdoor Navigation: Structured Environments Road following car: NAVLAB 1 3D vision for obstacle detection and avoidance Color vision for road following Pixel classification: determine the probability of every pixel to belong to the representation of the road Color: road reagions tend to appear more blue Texture: road regions tend to appear much smoother compared to non-road regions Hough-like transform: determine the roadvanishingpoint and orientation Reclassify pixels: taking into account the determined road edges.
30 Outdoor Navigation: Structured Environments ALLVIN: Autonomous Land Vehicle In A Neural Network (first reported in 1989) Idea: Learn driving by watching a human driver NN: Back propagation
31 Outdoor Navigation: Structured Environments Gaussian distribution of activations: xi =e d 2i / 10 xi = activation level output node i di = distance ith node and steering angle
32 Outdoor Navigation: Structured Environments Training with synthetic images Training on the fly No experiences of situations that require correction Forgetting due to long strait roads Solution: adding distorted images
33 Outdoor Navigation: Structured Environments ALVINN-VC (Virtual Camera) Allows the system to detect road changes and intersections before they get too close to the vehicle IRRE: Input Reconstruction Reliability Estimation Using the neural network's internal representation to reconstruct the original image Correlate this with the actual input to measure the network's reliability
34 Outline: Types of Navigation Absolute localization (Structured) Map-Based Indoor Map-Building Mapless (Unstructured) Navigation Outdoor Structured Unstructured Incremental localization Landmark tracking Optic flow Appearance based Object recognition
35 Outdoor Navigation: Unstructured Environments Outdoor environment with no regular properties Wandering / exploring Goal position: need for some map building and localization algorithm Vehicle centered coordinate frame External reference (e.g. an external camera) Global positioning reference: (e.g. mountain peaks, the sun)
36 Outdoor Navigation: Unstructured Environments Mars Pathfinder project Launched in December 1996, landed in July 1997
37 Outdoor Navigation: Unstructured Environments Human operators specified waypoints in 3D views of the landing site once a day Deadreckoning-based positioning Moving speed: 15 cm / s Hazard detection every 6.5 cm Maximum travel distance 10 m / day
38 Outdoor Navigation: Illumination Problem: differences in contrast and texture due to variations in illumination Use of color to compensate Lorigo et al. (1997) Exploring robot: Figure out position of obstacles. Vertical slices: histogram of intensity values (RGB, HSV, or BW) Compare with safe window
39 Questions?
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