Alexander J. B. Trevor

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We present an extension to our feature based mapping technique that allows for the use of planar surfaces such as walls, tables, counters, or other planar surfaces as landmarks in our mapper. These planar surfaces are measured both in 3D point clouds, as well as 2D laser scans. These sensing modalities compliment each other well, as they differ(More)
Segmentation is an important step in many perception tasks, such as object detection and recognition. We present an approach to organized point cloud segmentation and its application to plane segmentation, and euclidean clustering for tabletop object detection. The proposed approach is efficient and enables real-time plane segmentation for VGA resolution(More)
We present a 3D edge detection approach for RGB-D point clouds and its application in point cloud registration. Our approach detects several types of edges, and makes use of both 3D shape information and photometric texture information. Edges are categorized as occluding edges, occluded edges, boundary edges, high-curvature edges, and RGB edges. We exploit(More)
Object discovery and modeling have been widely studied in the computer vision and robotics communities. SLAM approaches that make use of objects and higher level features have also recently been proposed. Using higher level features provides several benefits: these can be more discriminative, which helps data association, and can serve to inform service(More)
Classification of spatial regions based on semantic information in an indoor environment enables robot tasks such as navigation or mobile manipulation to be spatially aware. The availability of contextual information can significantly simplify operation of a mobile platform. We present methods for automated recognition and classification of spaces into(More)
The goal of simultaneous localization and mapping (SLAM) is to compute the posterior distribution over landmark poses. Typically, this is made possible through the static world assumption - the landmarks remain in the same location throughout the mapping procedure. Some prior work has addressed this assumption by splitting maps into static and dynamic sets,(More)
Complex and structured landmarks like objects have many advantages over low-level image features for semantic mapping. Low level features such as image corners suffer from occlusion boundaries, ambiguous data association, imaging artifacts, and viewpoint dependance. Artificial landmarks are an unsatisfactory alternative because they must be placed in the(More)
Simultaneous Localization and Mapping (SLAM) aims to estimate the maximum likelihood map and robot pose based on a robot's control and sensor measurements. In structured environments, such as human environments, we might have additional domain knowledge that could be applied to produce higher quality mapping results. We present a method for using virtual(More)
Abslracl Semantic mapping aims to create maps that include meaningful features, both to robots nnd humans. We prescnt :10 extens ion to our feature based mapping technique that includes information about the locations of horizontl.lJ surfaces such as tables, shelves, or counters in the map. The surfaces a rc detected in 3D point clouds, the locations of(More)
Simultaneous Localization and Mapping (SLAM) is not a problem with a one-size-fits-all solution. The literature includes a variety of SLAM approaches targeted at different environments, platforms, sensors, CPU budgets, and applications. We propose OmniMapper, a modular multimodal framework and toolbox for solving SLAM problems. The system can be used to(More)