Corominas, A.; Vallve, J.; Solá, J.; Flores, I.; Andrade-Cetto, J. IEEE International Conference on Robotics and Automation p. 3161-3166 DOI: 10.1109/ICRA.2016.7487484 Data de presentació: 2016-06 Presentació treball a congrés
Localization is the key perceptual process closing the loop of autonomous navigation, allowing self-driving vehicles to operate in a deliberate way. To ensure robust localization, autonomous vehicles have to implement redundant estimation processes, ideally independent in terms of the underlying physics behind sensing principles. This paper presents a stereo radar odometry system, which can be used as such a redundant system, complementary to other odometry estimation processes, providing robustness for long-term operability. The presented work is novel with respect to previously published methods in that it contains: (i) a detailed formulation of the Doppler error and its associated uncertainty; (ii) an observability analysis that gives the minimal conditions to infer a 2D twist from radar readings; and (iii) a numerical analysis for optimal vehicle sensor placement. Experimental results are also detailed that validate the theoretical insights.
We present a decision theoretic approach to mobile robot exploration. The method evaluates the reduction of joint path and map entropy and computes a potential information field in robot configuration space using these joint entropy reduction estimates. The exploration trajectory is computed descending on the gradient of this field. The technique uses Pose SLAM as its estimation backbone. Very efficient kernel convolution mechanisms are used to evaluate entropy reduction for each sensor ray, and for each possible robot orientation, taking frontiers and obstacles into account. In the end, the computation of this field on the entire configuration space is shown to be very efficient. The approach is tested in simulations in a pair of publicly available datasets comparing favorably both in quality of estimates and in execution time against an RRT*-based search for the nearest frontier and also against a locally optimal exploration strategy. (C) 2014 Elsevier B.V. All rights reserved.
We propose a novel method for robotic exploration that evaluates paths that minimize both the joint path and map entropy per meter traveled. The method uses Pose SLAM to update the path estimate, and grows an RRT* tree to generate the set of candidate paths. This action selection mechanism contrasts with previous appoaches in which the action set was built heuristically from a sparse set of candidate actions. The technique favorably compares agains the classical frontier- based exploration and other Active Pose SLAM methods in simulations in a common publicly available dataset.
We propose a method for the computation of entropy decrease in C-space. These estimates are then used to evaluate candidate exploratory trajectories in the context of autonomous mobile robot mapping. The method evaluates both map and path entropy reduction and uses such estimates to compute trajectories that maximize coverage whilst min- imizing localization uncertainty, hence reducing map error. Very efficient kernel convolution mechanisms are used to evaluate entropy reduction at each sensor ray, and for each possible robot position and orientation, taking frontiers and obstacles into account. In contrast to most other exploration methods that evaluate entropy reduction at a small number of discrete robot configurations, we do it densely for the entire C-space. The computation of such dense entropy reduction maps opens the window to new exploratory strategies. In this paper we present two such strategies. In the first one we drive exploration through a gradient descent on the entropy decrease field. The second strategy chooses maximal entropy reduction configurations as candidate exploration goals, and plans paths to them using RRT*. Both methods use PoseSLAM as their estimation backbone, and are tested and compared with classical frontier-based exploration in simulations using common publicly available datasets.
Valencia, R.; Saarinen, J.; Andreasson, H.; Vallve, J.; Andrade-Cetto, J.; Lilienthal, A. IEEE International Conference on Robotics and Automation p. 3956-3962 DOI: 10.1109/ICRA.2014.6907433 Data de presentació: 2014 Presentació treball a congrés
Industrial environments are rarely static and often their configuration is continuously changing due to the material transfer flow. This is a major challenge for infrastructure free localization systems. In this paper we address this challenge by introducing a localization approach that uses a dual- timescale approach. The proposed approach - Dual-Timescale Normal Distributions Transform Monte Carlo Localization (DT- NDT-MCL) - is a particle filter based localization method, which simultaneously keeps track of the pose using an apriori known static map and a short-term map. The short-term map is continuously updated and uses Normal Distributions Transform Occupancy maps to maintain the current state of the environment. A key novelty of this approach is that it does not have to select an entire timescale map but rather use the best timescale locally. The approach has real-time performance and is evaluated using three datasets with increasing levels of dynamics. We compare our approach against previously pro- posed NDT-MCL and commonly used SLAM algorithms and show that DT-NDT-MCL outperforms competing algorithms with regards to accuracy in all three test cases.
We present a mobile robot exploration strategy that computes trajectories that minimize both path and map entropies. The method evaluates joint entropy reduction and computes a potential ¿eld in robot con¿guration space using these joint entropy reduction estimates. The exploration trajectory is computed descending on the gradient of these ¿eld. The technique uses Pose SLAM as its estimation backbone. Very ef¿cient kernel convolution mechanisms are used to evaluate entropy reduction for each sensor ray, and for each possible robot orientation, taking frontiers and obstacles into account. In the end, the computation of this ¿eld on the entire C-space is shown to be very efficient computationally. The approach is tested in simulations in a common publicly available dataset comparing favorably both in quality of estimates and execution time against another entropy reduction strategy that uses occupancy maps
En las titulaciones de ingeniería es cada vez más
común incorporar asignaturas basadas en la resolución de pequeños proyectos. Adicionalmente el ámbito de la ingeniería industrial es un ámbito
pluridisciplinar en el que es muy habitual combinar diversas disciplinas. En este trabajo se presentan los resultados obtenidos y mecanismos utilizados durante un proyecto final de carrera realizado en la ETSEIB y que es ejemplo de integracin de diferentes tecnologías. Este proyecto puede ser interés como ejemplo en el momento de formular problemas o definir proyecto multidisciplinares en este ámbito.