Sensing requirements for a 13,000 km intercontinental autonomous drive

Broggi, Alberto; Bombini, Luca; Cattani, Silvia; Cerri, Pietro; Fedriga, Rean Isabella

doi:10.1109/ivs.2010.5548026

Cited by 34 publications

(23 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The most time-consuming step of the SGM algorithm is path accumulation, since it must be performed for each pixel, disparity, and path: to speed up the processing, for each path direction the pixels are split into several independent slices that are processed in parallel; moreover only the accumulated value is saved into memory, while temporary data needed for incremental processing is kept into the CPU registers 1 . Finally, when computing the results of Eq.…”

Section: A Stereo Reconstructionmentioning

confidence: 99%

Stereo obstacle detection in challenging environments: The VIAC experience

Broggi

Buzzoni

Felisa

et al. 2011

2011 IEEE/RSJ International Conference on Intelligent Robots and Systems

Self Cite

View full text Add to dashboard Cite

Abstract-Obstacle detection by means of stereo-vision is a fundamental task in computer vision, which has spurred a lot of research over the years, especially in the field of vehicular robotics. The information provided by this class of algorithms is used both in driving assistance systems and in autonomous vehicles, so the quality of the results and the processing times become critical, as detection failures or delays can have serious consequences. The obstacle detection system presented in this paper has been extensively tested during VIAC, the VisLab Intercontinental Autonomous Challenge [1], [2], which has offered a unique chance to face a number of different scenarios along the roads of two continents, in a variety of conditions; data collected during the expedition has also become a reference benchmark for further algorithm improvements.

show abstract

Section: A Stereo Reconstructionmentioning

confidence: 99%

Stereo obstacle detection in challenging environments: The VIAC experience

Broggi

Buzzoni

Felisa

et al. 2011

2011 IEEE/RSJ International Conference on Intelligent Robots and Systems

Self Cite

View full text Add to dashboard Cite

show abstract

“…where µ (c) has been defined above, and c is the distance from R to the cell 3 . In practice, for two cells with equal µ (c) = 0, the nearest one will yield the highest potential.…”

Section: System Characteristicsmentioning

confidence: 99%

“…A great amount of robotics research focuses on vehicle guidance, with the goal of automatically reproducing the tasks usually performed by humans [2], [3]. Among others, an important task is obstacle avoidance, i.e., computing a control such that the trajectory generated is collision-free, and drives the robot to the goal [4].…”

Section: Introductionmentioning

confidence: 99%

Visual navigation with obstacle avoidance

Cherubini

Chaumette

2011

2011 IEEE/RSJ International Conference on Intelligent Robots and Systems

View full text Add to dashboard Cite

Abstract-We present and validate a framework for visual navigation with obstacle avoidance. The approach was originally designed in [1], but major improvements and real outdoor experiments are added here. Visual navigation consists of following a path, represented as an ordered set of key images, that have been acquired in a preliminary teaching phase. While following such path, the robot is able to avoid new obstacles which were not present during teaching, and which are sensed by a range scanner. We guarantee that collision avoidance and navigation are achieved simultaneously by actuating the camera pan angle, in the presence of obstacles, to maintain scene visibility as the robot circumnavigates the obstacle. The circumnavigation verse and the collision risk are estimated using a potential vector field derived from an occupancy grid. The framework can also deal with unavoidable obstacles, which make the robot decelerate and eventually stop.

show abstract

“…Today's vehicle-like robots have more complex functions with features of object detection and positioning capabilities Stanek et al [9], specially longterm navigational tasks Broggi et al [8]. Numerous kinematic planners that compute the shortest manoeuvring feasible path for vehicles explicitly considering vehicle dynamics is found in Moriwaki and Tanaka [12]; and similarly Werling et al [6] presented a search for an optimal path using dynamic simulations to determine the traversable or cost of specific terrain segments.…”

Section: Introductionmentioning

confidence: 99%

Exponential Fields Formulation for WMR Navigation

Martínez‐García

Torres-Cordoba

2012

Applied Bionics and Biomechanics

View full text Add to dashboard Cite

Abstract. In this manuscript, an autonomous navigation algorithm for wheeled mobile robots (WMR) operating in dynamic environments (indoors or structured outdoors) is formulated. The planning scheme is of critical importance for autonomous navigational tasks in complex dynamic environments. In fast dynamic environments, path planning needs algorithms able to sense simultaneously a diversity of obstacles, and use such sensory information to improve real-time navigation control, while moving towards a desired goal destination. The framework tackles 4 issues: 1) Reformulation of the Social Force Model (SFM) adapted to WMR; 2) the cohesion of a general inertial scheme to represents motion in any coordinate system; 3) control of actuators rotational speed as a general model regardless kinematic restrictions; 4) assuming detection of features (obstacles/goals), adaptive numeric weights are formulated to affect navigational exponential components. Simulation and experimental outdoors results are presented to show the feasibility of the proposed framework.

show abstract

Sensing requirements for a 13,000 km intercontinental autonomous drive

Cited by 34 publications

References 5 publications

Stereo obstacle detection in challenging environments: The VIAC experience

Stereo obstacle detection in challenging environments: The VIAC experience

Visual navigation with obstacle avoidance

Exponential Fields Formulation for WMR Navigation

Contact Info

Product

Resources

About