A Stereo Perception Framework for Autonomous Vehicles

Kemsaram, Narsimlu; Das, Anweshan; Dubbelman, Gijs

doi:10.1109/vtc2020-spring48590.2020.9128899

Cited by 13 publications

(5 citation statements)

References 13 publications

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“…This depth information allows for the creation of a 3D representation of the scene. Stereo3D is critical to enabling autonomous capabilities in a wide range of fields including robotics [29,30], autonomous vehicles [9,31], and 3D modeling [12,32,33].…”

Section: Stereo Reconstructionmentioning

confidence: 99%

UAV-Borne Mapping Algorithms for Low-Altitude and High-Speed Drone Applications

Zhang,

Wolek,

Willis

2024

Sensors

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This article presents an analysis of current state-of-the-art sensors and how these sensors work with several mapping algorithms for UAV (Unmanned Aerial Vehicle) applications, focusing on low-altitude and high-speed scenarios. A new experimental construct is created using highly realistic environments made possible by integrating the AirSim simulator with Google 3D maps models using the Cesium Tiles plugin. Experiments are conducted in this high-realism simulated environment to evaluate the performance of three distinct mapping algorithms: (1) Direct Sparse Odometry (DSO), (2) Stereo DSO (SDSO), and (3) DSO Lite (DSOL). Experimental results evaluate algorithms based on their measured geometric accuracy and computational speed. The results provide valuable insights into the strengths and limitations of each algorithm. Findings quantify compromises in UAV algorithm selection, allowing researchers to find the mapping solution best suited to their application, which often requires a compromise between computational performance and the density and accuracy of geometric map estimates. Results indicate that for UAVs with restrictive computing resources, DSOL is the best option. For systems with payload capacity and modest compute resources, SDSO is the best option. If only one camera is available, DSO is the option to choose for applications that require dense mapping results.

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Section: Stereo Reconstructionmentioning

confidence: 99%

UAV-Borne Mapping Algorithms for Low-Altitude and High-Speed Drone Applications

Zhang,

Wolek,

Willis

2024

Sensors

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“…Cameras can offer a wide range of configurations in resolution, frame rate, size and optics parameters. Moreover, stereocameras are effective sensors in the self-driving vehicles since they can also be used to estimate depth map from images, enabling further obstacle detection and trajectory planning algorithms [14][15]. Nevertheless, camera-based sensors have some drawbacks for autonomous driving tasks under varying light and visibility conditions and with scenes with a high dynamic range, such as in entering or exiting a tunnel [13].…”

Section: Issn 1335-4205 (Print Version) Issn 2585-7878 (Online Version)mentioning

confidence: 99%

Redundant Multi-Object Detection for Autonomous Vehicles in Structured Environments

Feraco¹,

Bonfitto²,

Amati³

et al. 2022

Komunikácie

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This paper presents a redundant multi-object detection method for autonomous driving, exploiting a combination of Light Detection and Ranging (LiDAR) and stereocamera sensors to detect different obstacles. These sensors are used for distinct perception pipelines considering a custom hardware/software architecture deployed on a self-driving electric racing vehicle. Consequently, the creation of a local map with respect to the vehicle position enables development of further local trajectory planning algorithms. The LiDAR-based algorithm exploits segmentation of point clouds for the ground filtering and obstacle detection. The stereocamerabased perception pipeline is based on a Single Shot Detector using a deep learning neural network. The presented algorithm is experimentally validated on the instrumented vehicle during different driving maneuvers.

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“…In this technique, the length of the objects may be unknown. An example of stereo vision for a 3D vision reconstruction was presented by Kemsaram et al [129], who used three deep neural networks simultaneously to perform free-space detection, as well as lane boundary detection and object detection on image frames captured using AV's stereo cameras.…”

Section: A Camera Sensorsmentioning

confidence: 99%

Autonomous Driving: A Survey of Technological Gaps Using Google Scholar and Web of Science Trend Analysis

Hacohen

Medina

Shoval

2022

IEEE Trans. Intell. Transport. Syst.

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Autonomous Driving (AD) introduces dramatic changes to the way we travel. This emerging technology has the potential to impact the transportation sector across a wide array of categories including safety, efficiency, congestion, legislation, and travel behavior. In this survey, we review the main issues involved in AD as discussed in the literature, and shed light on topics that we consider requiring further development based on Google Scholar and the Web of Science (WoS) data base. The paper also provides the results of research trends related to Autonomous Driving based on analysis of the number of search results listed in google trends. According to our research, the fields of Vehicle-to-Vehicle (V2V) and Vehicle-to-Cloud (V2C) networking are of higher interest due to the technological gaps and standardization processes. In addition, cyber and security research is in acceleration due to its importance.

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A Stereo Perception Framework for Autonomous Vehicles

Cited by 13 publications

References 13 publications

UAV-Borne Mapping Algorithms for Low-Altitude and High-Speed Drone Applications

UAV-Borne Mapping Algorithms for Low-Altitude and High-Speed Drone Applications

Redundant Multi-Object Detection for Autonomous Vehicles in Structured Environments

Autonomous Driving: A Survey of Technological Gaps Using Google Scholar and Web of Science Trend Analysis

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