Robust curb and ramp detection for safe parking using the Canesta TOF camera

Gallo, Orazio; Manduchi, Roberto; Rafii, Abbas

doi:10.1109/cvprw.2008.4563165

Cited by 38 publications

(25 citation statements)

References 31 publications

(33 reference statements)

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“…In [23] Acharya et al describe the system design of a ToF camera for backup obstacle detection. In [24] the same group presents an application of a similar camera for the detection of curves and ramps also in parking settings. A modified Ransac algorithm, that uses only the best inliers, is used to find the best fitting of the planar patches that model the environment.…”

Section: Scene-related Tasksmentioning

confidence: 99%

“…3D at high rate SR3 + inertial Swadzba et al [22] 3D mapping in dynamic env. 3D at high rate/Registered depth-intensity SR3 (depth + intensity) Acharya et al [23] Safe car parking Improved depth range/3D at high rate Canesta Gallo et al [24] Gortuk et al [25] Object classification (airbag app.) light/texture/shadow independence Canesta Yuan et al [26] Navigation and obst.…”

Section: Scene-related Tasksmentioning

confidence: 99%

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ToF cameras for active vision in robotics

Alenyà

Foix

Torras

2014

Sensors and Actuators A: Physical

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ToF cameras are now a mature technology that is widely being adopted to provide sensory input to robotic applications. Depending on the nature of the objects to be perceived and the viewing distance, we distinguish two groups of applications: those requiring to capture the whole scene and those centered on an object. It will be demonstrated that it is in this last group of applications, in which the robot has to locate and possibly manipulate an object, where the distinctive characteristics of ToF cameras can be better exploited.After presenting the physical sensor features and the calibration requirements of such cameras, we review some representative works highlighting for each one which of the distinctive ToF characteristics have been more essential. Even if at low resolution, the acquisition of 3D images at frame-rate is one of the most important features, as it enables quick background/foreground segmentation. A common use is in combination with classical color cameras. We present three developed applications, using a mobile robot and a robotic arm, to exemplify with real images some of the stated advantages.

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Section: Scene-related Tasksmentioning

confidence: 99%

Section: Scene-related Tasksmentioning

confidence: 99%

ToF cameras for active vision in robotics

Alenyà

Foix

Torras

2014

Sensors and Actuators A: Physical

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“…For example a state-of-the-art region-based method for range image segmentation does not give useful results on data from a SwissRanger [6]. Considering the elementary problems resulting from the poor data quality of this promising sensor type, it does not seem to be coincidental that several researchers currently work on getting segmentation algorithms more robust for range camera data [7], [8], [9].…”

Section: Related Workmentioning

confidence: 99%

Robust localization of furniture parts by integrating depth and intensity data suitable for range sensors with varying image quality

Meisner

Schmidt-Rohr

Lösch

et al. 2011

2011 15th International Conference on Advanced Robotics (ICAR)

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In this paper we present an approach to localize planar furniture parts in 3D range camera data for autonomous robot manipulation, that estimates both their six degree of freedom (DoF) poses and their dimensions. Range cameras are a promising sensor category for mobile robotics. Unfortunately, many of them come with a considerable measurement noise, that leads to difficulties when trying to detect objects or their parts e.g. using canonical methods for range image segmentation. In contrast, our approach is able to overcome these issues by combining concepts of 2D and 3D computer vision as well as integrating intensity and depth data on several levels of abstraction. Therefore it is not restricted to range sensors with high image quality and scales on cameras with lower image quality, too. This concept is generic and has been implemented for elliptical object parts as a proof of concept.

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“…레이저 스캐너를 사용하면 빛의 변화에 관계 없이 센서의 주변환경에 대한 정 확한 거리 정보를 획득할 수 있고, 획득한 거리정보에 허프 변환(hough transform)이나 히스토그램에 기반한 연석 탐지 기 법을 적용할 수 있다 [5,6]. 그밖에 TOF-image 센서나 3D PMD 센서를 이용하여 연석을 탐지하기도 한다 [7,8]. distribution of predicted range data, and (b) predicted range data and real measurement range data, and (c) standard deviation as function of curb height.…”

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Curb Detection and Following in Various Environments by Adjusting Tilt Angle of a Laser Scanner

Lee¹,

Lee²,

Song³

et al. 2010

Journal of Institute of Control, Robotics and Systems

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When a robot navigates in an outdoor environment, a curb or a sidewalk separated from the road can be used as a robust feature. However, most algorithms could detect the curb only in the straight road, and could not detect highly curved corners, ramps, and so on. This paper proposes an algorithm which enables the robot to detect and follow the curbs in various types of roads. In the proposed method, the robot tilts a laser scanner and computes the error between the predicted and the measured distances to the road in front of the robot. Based on this error, the curbs at corners and curves can be classified. It is also difficult to detect a curb near a ramp because of its low height. In this case, the robot also tilts a laser scanner to detect the curb beyond the ramp. Once the robot classifies the road into the curve, corner, ramp, the robot selects the proper navigation strategies depending on the classified road types and is able to continue to detect and follow the curb. The results of a series of experiments show that the robot can stably detect and follows the curb in curves, corners and ramps as well as the straight road.

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Robust curb and ramp detection for safe parking using the Canesta TOF camera

Abstract: Abstract

Cited by 38 publications

References 31 publications

ToF cameras for active vision in robotics

ToF cameras for active vision in robotics

Robust localization of furniture parts by integrating depth and intensity data suitable for range sensors with varying image quality

Curb Detection and Following in Various Environments by Adjusting Tilt Angle of a Laser Scanner

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