Visual Terrain Traversability Estimation Using a Combined Slope/Elevation Model

Braun, Tim; Bitsch, Henning; Berns, Karsten

doi:10.1007/978-3-540-85845-4_22

Cited by 15 publications

(12 citation statements)

References 3 publications

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“…In terms of slope the Ravon project uses a stereo camera system and fits small planes into the scene [5]. The normal gives a hint for the slope at the plane's position.…”

Section: Representation and Navigation Strategymentioning

confidence: 99%

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Perception of Environment Properties Relevant for Off-road Navigation

Renner

Föhst

Berns

2009

Autonome Mobile Systeme 2009

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Abstract. In this paper a set of physical properties is presented that can be utilized for save and efficient navigation in unstructured terrain. This set contains properties of positive obstacles, i.e. flexibility, shape, dimensions, etc. as well as properties of negative obstacles and ground, i.e. slope, carrying capacity, slippage, etc. By means of these properties a classifier is developed that supports the discrimination from traversable to non-traversable areas. Furthermore, an overview of different sensor systems, that can be employed to determine some these properties, is given.

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“…In terms of slope the Ravon project uses a stereo camera system and fits small planes into the scene [5]. The normal gives a hint for the slope at the plane's position.…”

Section: Representation and Navigation Strategymentioning

confidence: 99%

“…to texture (LBP), color (HSV) and variance (i.e. contrast) [6]. The resulting descriptors are classified using the knn algorithm and the knowledge gained from observing the obstacle avoidance.…”

Section: Representation and Navigation Strategymentioning

confidence: 99%

Perception of Environment Properties Relevant for Off-road Navigation

Renner

Föhst

Berns

2009

Autonome Mobile Systeme 2009

Self Cite

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“…These features allow the performance of a reliable terrain traversability assessment [1] on the recreated environment model. Although such processing of the environment on 3D models has been widely adopted in the literature [4], [7], only recent examples exploit photogrammetry from airborne imagery for this purpose [8], [9].…”

Section: Introductionmentioning

confidence: 99%

Coverage path planning for a flock of aerial vehicles to support autonomous rovers through traversability analysis

et al. 2019

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In rough terrains, such as landslides or volcanic eruptions, it is extremely complex to plan safe trajectories for an Unmanned Ground Vehicle (UGV), since both robot stability and path execution feasibility must be guaranteed. In this paper, we present a complete solution for the autonomous navigation of ground vehicles in the mentioned scenarios. The proposed solution integrates three different aspects. The first is the coverage path planning for the definition of UAV trajectories for aerial imagery acquisition. The collected images are used for the photogrammetric reconstruction of the considered area. The second aspect is the adoption of a flock of UAVs to implement the coverage in a parallel way. In fact, when non-coverable zones are present, decomposition of the whole area to survey is performed. A solution to assign the different regions among the flying vehicles composing the team is presented. The last aspect is the path planning of the ground vehicle by means of a traversability analysis performed on the terrain 3D model. The computed paths are optimal in terms of the difficulty of moving across the rough terrain. The results of each step within the overall approach are shown.

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“…Braun adopts a stereo vision based terrain traversability estimation method for off-road mobile robots. The method models surrounding terrain using either sloped planes or a digital elevation model, based on the availability of suitable input data 7 . Howard gives novel techniques for realtime terrain characterization and assessment of terrain traversability for a field mobile robot using a vision system and artificial neural networks.…”

Section: Introductionmentioning

confidence: 99%

Support Vector Machine Based Robotic Traversability Prediction with Vision Features

Cui¹,

Guo²,

Zhang³

et al. 2013

IJCIS

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This paper presents a novel method on building relationship between the vision features of the terrain images and the terrain traversability which manifests the difficulty of field robot traveling across one terrain. Vision features of the image are extracted based on color and texture. The travesability is labeled with the relative vibration. The support vector machine regression method is adopted to build up the inner relationship between them. In order to avoid the over-learning during training, k-fold method is used and average mean square error is defined as the target minimized to get the optimal parameters based on parameter space grid method. For the traveling smoothness of field robot, the original traversability prediction is transformed to computed traversability prediction based on different initial sub-regions. The optimal path is given by minimizing the sum of computed traversability prediction of all sub-regions in each path. Three experiments are discussed to demonstrate the effectiveness and efficiency of the method mentioned in this paper.

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Visual Terrain Traversability Estimation Using a Combined Slope/Elevation Model

Cited by 15 publications

References 3 publications

Perception of Environment Properties Relevant for Off-road Navigation

Perception of Environment Properties Relevant for Off-road Navigation

Coverage path planning for a flock of aerial vehicles to support autonomous rovers through traversability analysis

Support Vector Machine Based Robotic Traversability Prediction with Vision Features

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