Fast and reliable obstacle detection and segmentation for cross-country navigation

Talukder, Ashit; Manduchi, Roberto; Rankin, Arturo L.; Matthies, Larry

doi:10.1109/ivs.2002.1188019

Cited by 77 publications

(76 citation statements)

References 12 publications

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“…Enlarging the projected size only makes sense if the point cloud is virtually aligned with the ground and the projection accounts for it (option 4), which apparently was not the case in refs. [7] and [12]. However, we acknowledge it is a natural assumption since we made it ourselves in ref.…”

Section: H T < |Ymentioning

confidence: 99%

Real-time obstacle detection using range images: processing dynamically-sized sliding windows on a GPU

2015

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SUMMARYAn efficient obstacle detection technique is required so that navigating robots can avoid obstacles and potential hazards. This task is usually simplified by relying on structural patterns. However, obstacle detection constitutes a challenging problem in unstructured unknown environments, where such patterns may not exist. Talukder et al. (2002, IEEE Intelligent Vehicles Symposium, pp. 610-618.) successfully derived a method to deal with such environments. Nevertheless, the method has a high computational cost and researchers that employ it usually rely on approximations to achieve real-time. We hypothesize that by using a graphics processing unit (GPU), the computing time of the method can be significantly reduced. Throughout the implementation process, we developed a general framework for processing dynamically-sized sliding windows on a GPU. The framework can be applied to other problems that require similar computation. Experiments were performed with a stereo camera and an RGB-D sensor, where the GPU implementations were compared to multi-core and single-core CPU implementations. The results show a significant gain in the computational performance, i.e. in a particular instance, a GPU implementation is almost 90 times faster than a single-core one.KEYWORDS: Obstacle detection; Autonomous navigation; Stereo vision; Graphics processing unit (GPU). IntroductionA mobile robot with autonomous navigation capabilities can serve many practical purposes. It can be a vehicle that drives itself carrying passengers and goods or an agricultural machine that frees individuals from tedious and dangerous tasks. To accomplish such useful feat, robots must sense the environment through sensors, detect transversable regions and move through them precisely and efficiently. However, autonomous navigation involves several technical challenges, which range from dealing with intrinsically noisy sensors to correctly identifying road signs.A crucial component of an autonomous navigation system is the obstacle detection module. Regarding range images, a popular approach is to approximate the ground surface using a planar model. 1, 2 Once the model parameters have been estimated, the distance from any point to the model can be calculated and, based on a threshold, points can be distinguished as either parts of the ground or obstacles. Although reasonable for indoor environments, this approach is not suitable for outdoor ones, where one may expect to find curved and hilly regions.Another popular approach 3, 4 relies on a transformation of the disparity/range image called Vdisparity map, which is essentially a lateral projection of the range image where the longitudinal road profile is expected to appear as a line segment. With the road longitudinal profile extracted,

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Section: H T < |Ymentioning

confidence: 99%

Real-time obstacle detection using range images: processing dynamically-sized sliding windows on a GPU

2015

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“…Most systems have focused on navigating a robot on a semi-structured road surface. Algorithms designed for unstructured terrain emphasize detection of geometric obstacles such as rocks or steep slopes [4], [5]. These papers have not addressed the issue of the soil itself being a hazard.…”

Section: Introductionmentioning

confidence: 99%

Vibration-based Terrain Analysis for Mobile Robots

Brooks

Iagnemma

Dubowsky

Proceedings of the 2005 IEEE International Conference on Robotics and Automation

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Abstract-Safe, autonomous mobility in rough terrain is an important requirement for planetary exploration rovers. Knowledge of local terrain properties is critical to ensure a rover's safety on slopes and uneven surfaces. This paper presents a method to classify terrain based on vibrations induced in the rover structure by wheel-terrain interaction during driving. Vibrations are measured using an accelerometer on the rover structure. The classifier is trained using labeled vibration data during an off-line learning phase. Linear discriminant analysis is used for on-line identification of terrain classes such as sand, gravel, or clay. This approach is experimentally validated on a laboratory testbed.

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“…Most approaches use vision or ladar sensors. In ladar-based methods, the focus is not on estimating the type of the ground surface and detecting non-geometric hazards, but on segmenting the ground surface from vegetation and from obstacles like rocks or trunks [2]- [5]. Vision-based approaches usually use visual features like color or texture for terrain classification [5]- [7].…”

Section: Introductionmentioning

confidence: 99%

Vibration-based Terrain Classification Using Support Vector Machines

Weiß

Fröhlich

Zell

2006

2006 IEEE/RSJ International Conference on Intelligent Robots and Systems

128

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Abstract-In outdoor environments, there is a variety of different types of ground surfaces. If some of them are slippery or bumpy, for example, the ground surface itself is a possible hazard for an autonomous mobile vehicle traversing the surface. Therefore, it is beneficial if the vehicle is able to estimate, which terrain it is currently traversing. Using this estimation, the vehicle can adapt its driving style to the terrain. In this paper, we present a method for terrain classification based on vibration induced in the vehicle's body. An accelerometer mounted on the vehicle measures the vibration perpendicular to the ground surface. We experimentally compare representations of the data based on the Fast Fourier Transform (FFT) and on the Power Spectral Density (PSD). Additionally, we suggest a simpler and more compact representation based on features calculated from the raw data vectors and a combination of this representation with the PSD. We train and classify the data with a Support Vector Machine (SVM). Experiments on a large real-world dataset containing seven different terrain types evaluate our approach.

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Fast and reliable obstacle detection and segmentation for cross-country navigation

Abstract: Obstacle detection (OD)

Cited by 77 publications

References 12 publications

Real-time obstacle detection using range images: processing dynamically-sized sliding windows on a GPU

Real-time obstacle detection using range images: processing dynamically-sized sliding windows on a GPU

Vibration-based Terrain Analysis for Mobile Robots

Vibration-based Terrain Classification Using Support Vector Machines

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