Localization of a mobile autonomous robot based on image analysis

This paper introduces an innovative method to solve the problem of self localization of a mobile autonomous robot, and in particular a case study is carried out for robot localization in a RoboCup field environment. The approach here described is completely different from other methods currently used in RoboCup, since it is only based on the use of images and does not involve the use of techniques like Monte Carlo or other probabilistic approaches. This method is simple, acceptably efficient for the purpose it was created, and uses a relatively low computational time to calculate.Fundação para a Ciência e Tecnologia (FCT) - projecto POSI/ROBO/43892/200

Localization of a mobile autonomous robot based on image analysis

This paper introduces an innovative method to solve the problem of self localization of a mobile autonomous robot, and in particular a case study is carried out for robot localization in a RoboCup field environment. The approach here described is completely different from other methods currently used in RoboCup, since it is only based on the use of images and does not involve the use of techniques like Monte Carlo or other probabilistic approaches. This method is simple, acceptably efficient for the purpose it was created, and uses a relatively low computational time to calculate.Fundação para a Ciência e a Tecnologia (FCT) - POSI/ROBO/43892/200

AUV SLAM and experiments using a mechanical scanning forward-looking sonar

Navigation technology is one of the most important challenges in the applications of autonomous underwater vehicles (AUVs) which navigate in the complex undersea environment. The ability of localizing a robot and accurately mapping its surroundings simultaneously, namely the simultaneous localization and mapping (SLAM) problem, is a key prerequisite of truly autonomous robots. In this paper, a modified-FastSLAM algorithm is proposed and used in the navigation for our C-Ranger research platform, an open-frame AUV. A mechanical scanning imaging sonar is chosen as the active sensor for the AUV. The modified-FastSLAM implements the update relying on the on-board sensors of C-Ranger. On the other hand, the algorithm employs the data association which combines the single particle maximum likelihood method with modified negative evidence method, and uses the rank-based resampling to overcome the particle depletion problem. In order to verify the feasibility of the proposed methods, both simulation experiments and sea trials for C-Ranger are conducted. The experimental results show the modified-FastSLAM employed for the navigation of the C-Ranger AUV is much more effective and accurate compared with the traditional methods

Attention and Anticipation in Fast Visual-Inertial Navigation

We study a Visual-Inertial Navigation (VIN) problem in which a robot needs to estimate its state using an on-board camera and an inertial sensor, without any prior knowledge of the external environment. We consider the case in which the robot can allocate limited resources to VIN, due to tight computational constraints. Therefore, we answer the following question: under limited resources, what are the most relevant visual cues to maximize the performance of visual-inertial navigation? Our approach has four key ingredients. First, it is task-driven, in that the selection of the visual cues is guided by a metric quantifying the VIN performance. Second, it exploits the notion of anticipation, since it uses a simplified model for forward-simulation of robot dynamics, predicting the utility of a set of visual cues over a future time horizon. Third, it is efficient and easy to implement, since it leads to a greedy algorithm for the selection of the most relevant visual cues. Fourth, it provides formal performance guarantees: we leverage submodularity to prove that the greedy selection cannot be far from the optimal (combinatorial) selection. Simulations and real experiments on agile drones show that our approach ensures state-of-the-art VIN performance while maintaining a lean processing time. In the easy scenarios, our approach outperforms appearance-based feature selection in terms of localization errors. In the most challenging scenarios, it enables accurate visual-inertial navigation while appearance-based feature selection fails to track robot's motion during aggressive maneuvers.Comment: 20 pages, 7 figures, 2 table

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available