Visual based localization of a legged robot with a topological representation

In this chapter we have presented the performance of a localization method of legged AIBO robots in not-engineered environments, using vision as an active input sensor. This method is based on classic markovian approach but it has not been previously used with legged robots in indoor office environments. We have shown that the robot is able to localize itself in real time even inenvironments with noise produced by the human activity in a real office. It deals with uncertainty in its actions and uses perceived natural landmarks of the environment as the main sensor inpu

Visual based localization for a Legged Robot

P. 708-715This paper presents a visual based localization mechanism for a legged robot. Our proposal, fundamented on a probabilistic approach, uses a precompiled topological map where natural landmarks like doors or ceiling lights are recognized by the robot using its on-board camera. Experiments have been conducted using the AIBO Sony robotic dog showing that it is able to deal with noisy sensors like vision and to approximate world models representing indoor of ce environments. The two major contributions of this work are the use of this technique in legged robots, and the use of an active camera as the main sensorS

An Hybrid Approach for Robust and Precise Mobile Robot Navigation with Compact Environment Modeling

In this paper a new localization approach combining the metric and topological paradigm is presented. The main idea is to connect local metric maps by means of a global topological map. This allows a compact environment model which does not require global metric consistency and permits both precision and robustness. The method uses a 360 degree laser scanner in order to extract lines for the metric localization and doors, discontinuities and hallways for the topological approach. The approach has been widely tested in a 50 x 25 m portion of the institute building with the new fully autonomous robot Donald Duck. 25 randomly generated test missions have been performed with a success ratio of 96% and a mean error at the goal point of 9 mm for an overall trajectory length of 1.15 km. Future work will focus on a similar hybrid approach for simultaneous localization and automatic mapping