Fast Object Motion Estimation Based on Dynamic Stixels

Morales, Nestor; Morell, Antonio; Toledo, Jonay; Sánchez, Leopoldo

doi:10.3390/s16081182

Cited by 10 publications

(3 citation statements)

References 45 publications

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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

Toledo

et al. 2023

Sensors

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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.

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Section: Introductionmentioning

confidence: 99%

Improving Mobile Robot Maneuver Performance Using Fractional-Order Controller

Acosta

Fariña

Toledo

et al. 2023

Sensors

Self Cite

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“…Robots and autonomous vehicles use depth maps as one of their main information sources for navigation, planning, etc [1,2]. One of the most affordable and powerful sensors is a stereo camera.…”

Section: Introductionmentioning

confidence: 99%

Real-time stereo semi-global matching for video processing using previous incremental information

Toledo

Lauer

Stiller

2021

J Real-Time Image Proc

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This paper presents an incremental stereo algorithm designed to calculate a real-time disparity image. The algorithm is designed for stereo video sequences and uses previous information to reduce computation time and improve disparity image quality. It is based on the semi-global matching stereo algorithm but modified to reuse previous calculation information. Storing and reusing this information not only reduces computation time but improves accuracy in a cost filtering scheme. Some tests are presented to compare the computation time and results of the algorithm, which show that it can achieve better results in terms of quality and time than standard algorithms for some scenarios.

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“…This approach uses the Semi-Global Matching (SGM) running on FPGA board and a GPU to compute dense optical ow for tracking purpose. Morales et al also presented an innovative object tracking method based on the Stixel world [20]. These works use image processing for detection and focus only on pedestrians and ignore other obstacles.…”

Section: Introductionmentioning

confidence: 99%

Obstacle Detection for Intelligent Transportation Systems Using Deep Stacked Autoencoder and <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math> </inline-formula>-Nearest Neighbor Scheme

Dairi¹,

Harrou²,

Sun³

et al. 2018

IEEE Sensors J.

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Obstacle detection is an essential element for the development of intelligent transportation systems so that accidents can be avoided. In this study, we propose a stereovisionbased method for detecting obstacles in urban environment. The proposed method uses a deep stacked auto-encoders (DSA) model that combines the greedy learning features with the dimensionality reduction capacity and employs an unsupervised k-nearest neighbors algorithm (KNN) to accurately and reliably detect the presence of obstacles. We consider obstacle detection as an anomaly detection problem. We evaluated the proposed method by using practical data from three publicly available datasets, the Malaga stereovision urban dataset (MSVUD), the Daimler urban segmentation dataset (DUSD), and Bahnhof dataset. Also, we compared the efficiency of DSA-KNN approach to the deep belief network (DBN)-based clustering schemes. Results show that the DSA-KNN is suitable to visually monitor urban scenes.

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Fast Object Motion Estimation Based on Dynamic Stixels

Cited by 10 publications

References 45 publications

Improving Mobile Robot Maneuver Performance Using Fractional-Order Controller

Improving Mobile Robot Maneuver Performance Using Fractional-Order Controller

Real-time stereo semi-global matching for video processing using previous incremental information

Obstacle Detection for Intelligent Transportation Systems Using Deep Stacked Autoencoder and <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math> </inline-formula>-Nearest Neighbor Scheme

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