Probabilistic Semantic Mapping with a Virtual Sensor for Building/Nature detection

Persson, Mattias; Duckett, Tom; Valgren, Christoffer; Lilienthal, Achim J.

doi:10.1109/cira.2007.382870

Cited by 19 publications

(22 citation statements)

References 15 publications

(21 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While mentioned researchers aimed to derive semantic concept from the functionality of the objects into the map, some others such as [8], [12] and [7], introduced properties of the regions as semantic label. [8] annotates an occupancy map with the properties of the regions, either "building" or "nature", through data from range scanner and vision.…”

Section: Related Workmentioning

confidence: 99%

Modeling of a Large Structured Environment

Shahbandi

Åstrand

2014

Advances in Autonomous Robotics Systems

View full text Add to dashboard Cite

Abstract. AIMS project attempts to link the logistic requirements of an intelligent warehouse and state of the art core technologies of automation, by providing an awareness of the environment to the autonomous systems and vice versa. In this work we investigate a solution for modeling the infrastructure of a structured environment such as warehouses, by the means of a vision sensor. The model is based on the expected pattern of the infrastructure, generated from and matched to the map. Generation of the model is based on a set of tools such as closed-form Hough transform, DBSCAN clustering algorithm, Fourier transform and optimization techniques. The performance evaluation of the proposed method is accompanied with a real world experiment.

show abstract

Section: Related Workmentioning

confidence: 99%

Modeling of a Large Structured Environment

Shahbandi

Åstrand

2014

Advances in Autonomous Robotics Systems

View full text Add to dashboard Cite

show abstract

“…The first two parameters reflect a possible error of 2 pixels between the robot position and the aerial image, and the third parameter allows, for example, each endpoint of a 10 pixel long 8 The limit 0.9 was chosen with respect to the probabilities used in the process of building the semantic map [2]. With this limit at least two positive building readings are needed for a single cell to be used in L M g .…”

Section: Tests Of the Local Segmentationmentioning

confidence: 99%

“…An example of such a map is given in Figure 2. For more details on this approach to probabilistic semantic mapping see [2].…”

Section: Wall Candidates From Ground Perspectivementioning

confidence: 99%

“…The semantic map we use is a probabilistic occupancy grid map with two classes: buildings and non-buildings [2]. The probabilistic semantic map is produced using an algorithm that fuses different sensor modalities.…”

Section: Wall Candidates From Ground Perspectivementioning

confidence: 99%

“…In [1] we defined a virtual sensor 2 . With an occupancy grid map and a virtual sensor learned to separate buildings from nonbuildings we have a method to build a probabilistic semantic map [2]. In [3] we showed how wall estimates extracted from this probabilistic semantic map could be used for detection of buildings in aerial images by their roof outlines.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fusion of aerial images and sensor data from a ground vehicle for improved semantic mapping

Persson

Duckett

Lilienthal

2008

Robotics and Autonomous Systems

Self Cite

View full text Add to dashboard Cite

This work investigates the use of semantic information to link ground level occupancy maps and aerial images. A ground level semantic map, which shows open ground and indicates the probability of cells being occupied by walls of buildings, is obtained by a mobile robot equipped with an omnidirectional camera, GPS and a laser range finder. This semantic information is used for local and global segmentation of an aerial image. The result is a map where the semantic information has been extended beyond the range of the robot sensors and predicts where the mobile robot can find buildings and potentially driveable ground.

show abstract

Improved Mapping and Image Segmentation by Using Semantic Information to Link Aerial Images and Ground-Level Information

Persson

Duckett

Lilienthal

Recent Progress in Robotics: Viable Robotic Service to Human

Self Cite

View full text Add to dashboard Cite

Abstract-This paper investigates the use of semantic information to link ground-level occupancy maps and aerial images. In the suggested approach a ground-level semantic map is obtained by a mobile robot equipped with an omnidirectional camera, differential GPS and a laser range finder. The mobile robot uses a virtual sensor for building detection (based on omnidirectional images) to compute the ground-level semantic map, which indicates the probability of the cells being occupied by the wall of a building. These wall estimates from a ground perspective are then matched with edges detected in an aerial image. The result is used to direct a region-and boundary-based segmentation algorithm for building detection in the aerial image. This approach addresses two difficulties simultaneously: 1) the range limitation of mobile robot sensors and 2) the difficulty of detecting buildings in monocular aerial images. With the suggested method building outlines can be detected faster than the mobile robot can explore the area by itself, giving the robot an ability to "see" around corners. At the same time, the approach can compensate for the absence of elevation data in segmentation of aerial images. Our experiments demonstrate that ground-level semantic information (wall estimates) allows to focus the segmentation of the aerial image to find buildings and produce a groundlevel semantic map that covers a larger area than can be built using the onboard sensors along the robot trajectory.

show abstract

Probabilistic Semantic Mapping with a Virtual Sensor for Building/Nature detection

Cited by 19 publications

References 15 publications

Modeling of a Large Structured Environment

Modeling of a Large Structured Environment

Fusion of aerial images and sensor data from a ground vehicle for improved semantic mapping

Improved Mapping and Image Segmentation by Using Semantic Information to Link Aerial Images and Ground-Level Information

Contact Info

Product

Resources

About