Generating automatic road network definition files for unstructured areas using a multiclass support vector machine

et al. 2016

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The stixel world is a simplification of the world in which obstacles are represented as vertical instances, called stixels, standing on a surface assumed to be planar. In this paper, previous approaches for stixel tracking are extended using a two-level scheme. In the first level, stixels are tracked by matching them between frames using a bipartite graph in which edges represent a matching cost function. Then, stixels are clustered into sets representing objects in the environment. These objects are matched based on the number of stixels paired inside them. Furthermore, a faster, but less accurate approach is proposed in which only the second level is used. Several configurations of our method are compared to an existing state-of-the-art approach to show how our methodology outperforms it in several areas, including an improvement in the quality of the depth reconstruction.

Section: Methodsmentioning

confidence: 99%

Fast Object Motion Estimation Based on Dynamic Stixels

Morales

Morell

et al. 2016

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“…The prototype includes an on board computer, sensors and software that turn it into an autonomous robot capable of transporting two passengers in non-structured environments. The vehicle localize itself [ 2 , 3 ], makes navigation decisions [ 4 , 5 ], detects obstacles [ 6 ], avoids them [ 7 , 8 ], and plans the best route in real time [ 9 ]. The robot applies this plan using a steering and velocity control, and the quality of the control limits the final performance of the vehicle.…”

Section: Introductionmentioning

confidence: 99%

Improving Mobile Robot Maneuver Performance Using Fractional-Order Controller

Acosta

Fariña

et al. 2023

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In this paper, the low-level velocity controller of an autonomous vehicle is studied. The performance of the traditional controller used in this kind of system, a PID, is analyzed. This kind of controller cannot follow ramp references without error, so when the reference implies a change in the speed, the vehicle cannot follow the proposed reference, and there is a significant difference between the actual and desired vehicle behaviors. A fractional controller is proposed which changes the ordinary dynamics allowing faster responses for small times, at the cost of slower responses for large times. The idea is to take advantage of this fact to follow fast setpoint changes with a smaller error than that obtained with a classic non-fractional PI controller. Using this controller, the vehicle can follow variable speed references with zero stationary error, significantly reducing the difference between reference and actual vehicle behavior. The paper presents the fractional controller, studies its stability in function of the fractional parameters, designs the controller, and tests its stability. The designed controller is tested on a real prototype, and its behavior is compared to a standard PID controller. The designed fractional PID controller overcomes the results of the standard PID controller.

“…The main advantage of odometry is that all localization information comes from the robot itself. Odometry information is always available and usually it is the only localization information when other sensors are not able to provide data, so a good odometry based localization system is always necessary and it is usually the first step to localization [ 11 , 12 ], obstacle detection [ 13 ], and navigation [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

Improving Odometric Accuracy for an Autonomous Electric Cart

Piñeiro

Arnay

et al. 2018

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In this paper, a study of the odometric system for the autonomous cart Verdino, which is an electric vehicle based on a golf cart, is presented. A mathematical model of the odometric system is derived from cart movement equations, and is used to compute the vehicle position and orientation. The inputs of the system are the odometry encoders, and the model uses the wheels diameter and distance between wheels as parameters. With this model, a least square minimization is made in order to get the nominal best parameters. This model is updated, including a real time wheel diameter measurement improving the accuracy of the results. A neural network model is used in order to learn the odometric model from data. Tests are made using this neural network in several configurations and the results are compared to the mathematical model, showing that the neural network can outperform the first proposed model.