Obstacle detection during day and night conditions using stereo vision

Dubbelman, Gijs; Mark, W. van der; Heuvel, J.C. van den; Groen, F.C.A.

doi:10.1109/iros.2007.4399055

Cited by 22 publications

(16 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The probability P(L u ) models a-priori world knowledge to constrain the labeling to avoid dispensable segments and physically unlikely situations. This world model offers a way to regularize the results for image-column optimality, whereas the methods of [7] and [8] potentially lead to suboptimal results, since they analyze data mostly locally. The details concerning P(L) are presented in [9].…”

Section: The Disparity Stixel Worldmentioning

confidence: 99%

“…In stereo vision, the disparity, which is analogous to depth, can be estimated densely and in real-time [6]. This gives a direct description of the geometry of the scene and it facilitates, for example, a separation of flat, drivable surfaces from erect obstacles [7], [8]. A state-of-the-art approach for this is called the Stixel World method [9].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Extending the Stixel World with online self-supervised color modeling for road-versus-obstacle segmentation

Sanberg

Dubbelman

2014

17th International IEEE Conference on Intelligent Transportation Systems (ITSC)

Self Cite

View full text Add to dashboard Cite

Section: The Disparity Stixel Worldmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Extending the Stixel World with online self-supervised color modeling for road-versus-obstacle segmentation

Sanberg

Dubbelman

2014

17th International IEEE Conference on Intelligent Transportation Systems (ITSC)

Self Cite

View full text Add to dashboard Cite

“…[6], obstacles were detected using dense 3D terrain data reconstructed from stereo disparities in the direction of image columns. [6], obstacles were detected using dense 3D terrain data reconstructed from stereo disparities in the direction of image columns.…”

Section: Previous Workmentioning

confidence: 99%

Mind the gap: detection and traversability analysis of terrain gaps using LIDAR for safe robot navigation

Sinha

Papadakis

2013

Robotica

View full text Add to dashboard Cite

SUMMARYSafe navigation of robotic vehicles is considered as a key pre-requisite of successful mission operations within highly adverse and unconstrained environments. While there has been extensive research in the perception of positive obstacles, little progress can be accredited to the field of negative obstacles. This paper hypostatizes an elaborative attempt to address the problem of negative obstacle detection and traversability analysis in the form of gaps by processing 3-dimensional range data. The domain of application concerns Urban Search and Rescue scenarios that reflect environments of increased complexity in terms of diverse terrain irregularities. To allow real-time performance and, in turn, timely prevention of unrecoverable robotic states, the proposed approach is based on the application of efficient image morphological operations for noise reduction and border following the detection and grouping of gaps. Furthermore, we reason about gap traversability, a concept that is novel within the field. Traversability assessments are based on features extracted through Principal Component Analysis by exploring the spatial distribution of the interior of the individual gaps or the orientation distribution of the corresponding contour. The proposed approach is evaluated within a realistic scenario of a tunnel car accident site and a challenging outdoor scenario. Using a contemporary Search and Rescue robot, we have performed extensive experiments under various parameter settings that allowed the robot to always detect the real gaps, and either optimally cross over those that were traversable or otherwise avoid them.

show abstract

“…In general the use of ground truth data within real-time temporal (i.e. video) evaluation of stereo is limited to comparison against ground plain and simple background/foreground separation models [11,12]. Following that purported in [8] we look to the concept of semantic stability, as a conduit to the ready and reliable segmentation of foreground scene object (e.g.…”

Section: Introductionmentioning

confidence: 99%

An empirical comparison of real-time dense stereo approaches for use in the automotive environment

Mroz

Breckon

2012

J Image Video Proc

View full text Add to dashboard Cite

In this work we evaluate the use of several real-time dense stereo algorithms as a passive 3D sensing technology for potential use as part of a driver assistance system or autonomous vehicle guidance. A key limitation in prior work in this area is that although significant comparative work has been done on dense stereo algorithms using de facto laboratory test sets only limited work has been done on evaluation in real world environments such as that found in potential automotive usage. This comparative study aims to provide an empirical comparison using automotive environment video imagery and compare this against dense stereo results drawn on standard test sequences in addition to considering the computational requirement against performance in real-time. We evaluate five chosen algorithms: Block Matching, Semi-Global Matching, No-Maximal Disparity, Cross-Based Local Approach, Adaptive Aggregation with Dynamic Programming. Our comparison shows a contrast between the results obtained on standard test sequences and those for automotive application imagery where a Semi-Global Matching approach gave the best empirical performance. From our study we can conclude that the noise present in automotive applications, can impact the quality of the depth information output from more complex algorithms (No-Maximal Disparity, CrossBased Local Approach, Adaptive Aggregation with Dynamic Programming) resulting that in practice the disparity maps produced are comparable with those of simpler approaches such as Block Matching and Semi-Global Matching which empirically perform better in the automotive environment test sequences. This empirical result on automotive environment data contradicts the comparative result found on standard dense stereo test sequences using a statistical comparison methodology leading to interesting observations regarding current relative evaulation approaches.

show abstract

Obstacle detection during day and night conditions using stereo vision

Cited by 22 publications

References 16 publications

Extending the Stixel World with online self-supervised color modeling for road-versus-obstacle segmentation

Extending the Stixel World with online self-supervised color modeling for road-versus-obstacle segmentation

Mind the gap: detection and traversability analysis of terrain gaps using LIDAR for safe robot navigation

An empirical comparison of real-time dense stereo approaches for use in the automotive environment

Contact Info

Product

Resources

About