1,554 research outputs found
Visual SLAM using straight lines
The present thesis is focuses on the problem of Simultaneous Localisation and
Mapping (SLAM) using only visual data (VSLAM). This means to concurrently estimate the position of a moving camera and to create a consistent map of the environment.
Since implementing a whole VSLAM system is out of the scope of a degree thesis, the main aim is to improve an existing visual SLAM system by complementing the commonly used point features with straight line primitives. This enables more accurate localization in environments with few feature points, like corridors.
As a foundation for the project, ScaViSLAM by Strasdat et al. is used, which is a state-of-the-art real-time visual SLAM framework. Since it currently only supports Stereo and RGB-D systems, implementing a Monocular approach will be researched as well as an integration of it as a ROS package in order to deploy it on a mobile robot.
For the experimental results, the Care-O-bot service robot developed by Fraunhofer IPA will be used
Fast Cylinder and Plane Extraction from Depth Cameras for Visual Odometry
This paper presents CAPE, a method to extract planes and cylinder segments
from organized point clouds, which processes 640x480 depth images on a single
CPU core at an average of 300 Hz, by operating on a grid of planar cells.
While, compared to state-of-the-art plane extraction, the latency of CAPE is
more consistent and 4-10 times faster, depending on the scene, we also
demonstrate empirically that applying CAPE to visual odometry can improve
trajectory estimation on scenes made of cylindrical surfaces (e.g. tunnels),
whereas using a plane extraction approach that is not curve-aware deteriorates
performance on these scenes. To use these geometric primitives in visual
odometry, we propose extending a probabilistic RGB-D odometry framework based
on points, lines and planes to cylinder primitives. Following this framework,
CAPE runs on fused depth maps and the parameters of cylinders are modelled
probabilistically to account for uncertainty and weight accordingly the pose
optimization residuals.Comment: Accepted to IROS 201
Probabilistic Search for Object Segmentation and Recognition
The problem of searching for a model-based scene interpretation is analyzed within a probabilistic framework. Object models are formulated as generative models for range data of the scene. A new statistical criterion, the truncated object probability, is introduced to infer an optimal sequence of object hypotheses to be evaluated for their match to the data. The truncated probability is partly determined by prior knowledge of the objects and partly learned from data. Some experiments on sequence quality and object segmentation and recognition from stereo data are presented. The article recovers classic concepts from object recognition (grouping, geometric hashing, alignment) from the probabilistic perspective and adds insight into the optimal ordering of object hypotheses for evaluation. Moreover, it introduces point-relation densities, a key component of the truncated probability, as statistical models of local surface shape
Probabilistic Search for Object Segmentation and Recognition
The problem of searching for a model-based scene interpretation is analyzed
within a probabilistic framework. Object models are formulated as generative
models for range data of the scene. A new statistical criterion, the truncated
object probability, is introduced to infer an optimal sequence of object
hypotheses to be evaluated for their match to the data. The truncated
probability is partly determined by prior knowledge of the objects and partly
learned from data. Some experiments on sequence quality and object segmentation
and recognition from stereo data are presented. The article recovers classic
concepts from object recognition (grouping, geometric hashing, alignment) from
the probabilistic perspective and adds insight into the optimal ordering of
object hypotheses for evaluation. Moreover, it introduces point-relation
densities, a key component of the truncated probability, as statistical models
of local surface shape.Comment: 18 pages, 5 figure
Online Object Tracking with Proposal Selection
Tracking-by-detection approaches are some of the most successful object
trackers in recent years. Their success is largely determined by the detector
model they learn initially and then update over time. However, under
challenging conditions where an object can undergo transformations, e.g.,
severe rotation, these methods are found to be lacking. In this paper, we
address this problem by formulating it as a proposal selection task and making
two contributions. The first one is introducing novel proposals estimated from
the geometric transformations undergone by the object, and building a rich
candidate set for predicting the object location. The second one is devising a
novel selection strategy using multiple cues, i.e., detection score and
edgeness score computed from state-of-the-art object edges and motion
boundaries. We extensively evaluate our approach on the visual object tracking
2014 challenge and online tracking benchmark datasets, and show the best
performance.Comment: ICCV 201
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