This thesis describes the design and implementation of a mobile robot which is able to perform a structural interpretation of indoor environments, using only visual and proprioceptive sensory information. The desired behaviour is real-time navigation based on this interpretation, instead of a reactive approach. The design is guided by a predictive criterion: the system must anticipate the consequences of its actions, showing a certain predictive understanding of the scene in which it moves.Facultad de Informátic

López de Teruel Alcolea, Pedro E.

Journal of Computer Science and Technology

English

Servicio de Difusión de la Creación Intelectual

 Thesis Overview:  "A High-level Perception Architecture: Real-time Visual Navigation  for Autonomous Robots in Structured Environments" Pedro E. López de Teruel Alcolea Universidad de Murcia (Spain) Departamento de Ingeniería y Tecnología de Computadores Advisors: A. Ruiz García y J.M. García Carrasco July 25, 2003 pedroe@ditec.um.es  This thesis describes the design and implementation of a mobile robot which is able to perform a structural interpretation of indoor environments, using only visual and proprioceptive sensory information. The desired behaviour is real-time navigation based on this interpretation, instead of a reactive approach. The design is guided by a predictive criterion: the system must anticipate the consequences of its actions, showing a certain predictive understanding of the scene in which it moves.  We propose solutions for all perception levels. First, we have developed a segment extraction technique augmented with color information, of remarkable expressive power and computational efficiency. The method is appropriate for real-time visual geometry applications. If the expressive power of raw segments is augmented with robust color information from their two sides, the most relevant geometric and photometric structure in the image can be concisely captured. In this thesis we describe an efficient algorithm to compute this kind of representation, which can be successfully exploited in several projective geometry problems, such as 3D reconstruction, motion estimation, calibration, and in interpretation related tasks, all of them critical for robot navigation. We also show how these enhanced primitives are powerful enough to recover a very acceptable approximation of the original image, especially for partially structured scenes, like interior of buildings, man-made objects, and so on.  To infer euclidean properties of space we present a collection of autocalibration methods based on the projective geometry formalism. These techniques can be applied on-line during robot operation, using features extracted from the natural environment (thus avoiding the need of calibration patterns, typical in classical approximations). The distinctive feature of the proposed methods is that they take advantage of the odometric information of the autonomous agent when solving for the intrinsic and extrinsic parameters of the camera.  High-level perception is solved through generation, tracking and confirmation of hypothesis about the environment, which are maintained in an stable internal representation tuned with the agent movements. This mechanism is characterized by a constant interaction between the bottom-up perceptive processes, guided by sensory stimuli, and the top-down ones, guided by the models. Using the estimated camera parameters, and following reasonable clues obtained from the geometric and color information of the segments, the robot can detect relevant planes (floor, walls, doors) in indoor environments. A calibrated sensor allows for the metric rectification of these planes. The system is then able to construct an abstract interpretation of the scene, which is used to categorize different high-level typical situations (rooms, corridors, corners, and so on), that allow the robot to exhibit a non-reactive behaviour, depending on abstract properties of the environment. Finally, the robot is able to accumulate the reconstruction of the visited environment (in the form of a 3D map) as it navigates, using odometry and tracking.   All these elements are integrated in an efficient, modular and flexible hardware-software architecture, in which action and perception cooperate to achieve the robustness and continuity needed by real world navigation. Our efficient implementation and integration of all the components of the system results in a very fast perception-action cycle (25 fps in a modern 2Ghz PC, including calibration, reconstruction, interpretation, and motion control). This is enough for robust navigation of the agent at approximate speeds of  20 cm/s in real scenarios. 

A high-level perception architecture: real-time visual navigation  for autonomous robots in structured environments

SEDICI - Repositorio de la UNLP

 
Thesis Overview: 
 
"A High-level Perception Architecture: Real-time Visual Navigation  
for Autonomous Robots in Structured Environments" 
Pedro E. López de Teruel Alcolea 
Universidad de Murcia (Spain) 
Departamento de Ingeniería y Tecnología de Computadores 
Advisors: A. Ruiz García y J.M. García Carrasco 
July 25, 2003 
pedroe@ditec.um.es 
 
This thesis describes the design and implementation of a mobile robot which is able to perform a structural 
interpretation of indoor environments, using only visual and proprioceptive sensory information. The desired 
behaviour is real-time navigation based on this interpretation, instead of a reactive approach. The design is 
guided by a predictive criterion: the system must anticipate the consequences of its actions, showing a certain 
predictive understanding of the scene in which it moves. 
 
We propose solutions for all perception levels. First, we have developed a segment extraction technique 
augmented with color information, of remarkable expressive power and computational efficiency. The method is 
appropriate for real-time visual geometry applications. If the expressive power of raw segments is augmented 
with robust color information from their two sides, the most relevant geometric and photometric structure in the 
image can be concisely captured. In this thesis we describe an efficient algorithm to compute this kind of 
representation, which can be successfully exploited in several projective geometry problems, such as 3D 
reconstruction, motion estimation, calibration, and in interpretation related tasks, all of them critical for robot 
navigation. We also show how these enhanced primitives are powerful enough to recover a very acceptable 
approximation of the original image, especially for partially structured scenes, like interior of buildings, man-
made objects, and so on. 
 
To infer euclidean properties of space we present a collection of autocalibration methods based on the projective 
geometry formalism. These techniques can be applied on-line during robot operation, using features extracted 
from the natural environment (thus avoiding the need of calibration patterns, typical in classical approximations). 
The distinctive feature of the proposed methods is that they take advantage of the odometric information of the 
autonomous agent when solving for the intrinsic and extrinsic parameters of the camera. 
 
High-level perception is solved through generation, tracking and confirmation of hypothesis about the 
environment, which are maintained in an stable internal representation tuned with the agent movements. This 
mechanism is characterized by a constant interaction between the bottom-up perceptive processes, guided by 
sensory stimuli, and the top-down ones, guided by the models. Using the estimated camera parameters, and 
following reasonable clues obtained from the geometric and color information of the segments, the robot can 
detect relevant planes (floor, walls, doors) in indoor environments. A calibrated sensor allows for the metric 
rectification of these planes. The system is then able to construct an abstract interpretation of the scene, which is 
used to categorize different high-level typical situations (rooms, corridors, corners, and so on), that allow the 
robot to exhibit a non-reactive behaviour, depending on abstract properties of the environment. Finally, the robot 
is able to accumulate the reconstruction of the visited environment (in the form of a 3D map) as it navigates, 
using odometry and tracking.  
 
All these elements are integrated in an efficient, modular and flexible hardware-software architecture, in which 
action and perception cooperate to achieve the robustness and continuity needed by real world navigation. Our 
efficient implementation and integration of all the components of the system results in a very fast perception-
action cycle (25 fps in a modern 2Ghz PC, including calibration, reconstruction, interpretation, and motion 
control). This is enough for robust navigation of the agent at approximate speeds of  20 cm/s in real scenarios. 


http://sedici.unlp.edu.ar/bitstream/handle/10915/9472/Documento_completo.pdf?sequence=1

A high-level perception architecture: real-time visual navigation for autonomous robots in structured environments

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