1,302 research outputs found
Efficient scene simulation for robust monte carlo localization using an RGB-D camera
This paper presents Kinect Monte Carlo Localization (KMCL), a new method for localization in three dimensional indoor environments using RGB-D cameras, such as the Microsoft Kinect. The approach makes use of a low fidelity a priori 3-D model of the area of operation composed of large planar segments, such as walls and ceilings, which are assumed to remain static. Using this map as input, the KMCL algorithm employs feature-based visual odometry as the particle propagation mechanism and utilizes the 3-D map and the underlying sensor image formation model to efficiently simulate RGB-D camera views at the location of particle poses, using a graphical processing unit (GPU). The generated 3D views of the scene are then used to evaluate the likelihood of the particle poses. This GPU implementation provides a factor of ten speedup over a pure distance-based method, yet provides comparable accuracy. Experimental results are presented for five different configurations, including: (1) a robotic wheelchair, (2) a sensor mounted on a person, (3) an Ascending Technologies quadrotor, (4) a Willow Garage PR2, and (5) an RWI B21 wheeled mobile robot platform. The results demonstrate that the system can perform robust localization with 3D information for motions as fast as 1.5 meters per second. The approach is designed to be applicable not just for robotics but other applications such as wearable computing
Fast Monte-Carlo Localization on Aerial Vehicles using Approximate Continuous Belief Representations
Size, weight, and power constrained platforms impose constraints on
computational resources that introduce unique challenges in implementing
localization algorithms. We present a framework to perform fast localization on
such platforms enabled by the compressive capabilities of Gaussian Mixture
Model representations of point cloud data. Given raw structural data from a
depth sensor and pitch and roll estimates from an on-board attitude reference
system, a multi-hypothesis particle filter localizes the vehicle by exploiting
the likelihood of the data originating from the mixture model. We demonstrate
analysis of this likelihood in the vicinity of the ground truth pose and detail
its utilization in a particle filter-based vehicle localization strategy, and
later present results of real-time implementations on a desktop system and an
off-the-shelf embedded platform that outperform localization results from
running a state-of-the-art algorithm on the same environment
Depth sensors in augmented reality solutions. Literature review
The emergence of depth sensors has made it possible to track – not only monocular
cues – but also the actual depth values of the environment. This is especially
useful in augmented reality solutions, where the position and orientation (pose) of
the observer need to be accurately determined. This allows virtual objects to be
installed to the view of the user through, for example, a screen of a tablet or augmented
reality glasses (e.g. Google glass, etc.). Although the early 3D sensors have
been physically quite large, the size of these sensors is decreasing, and possibly –
eventually – a 3D sensor could be embedded – for example – to augmented reality
glasses. The wider subject area considered in this review is 3D SLAM methods,
which take advantage of the 3D information available by modern RGB-D sensors,
such as Microsoft Kinect. Thus the review for SLAM (Simultaneous Localization
and Mapping) and 3D tracking in augmented reality is a timely subject. We also try
to find out the limitations and possibilities of different tracking methods, and how
they should be improved, in order to allow efficient integration of the methods to
the augmented reality solutions of the future.Siirretty Doriast
Depth sensors in augmented reality solutions. Literature review
The emergence of depth sensors has made it possible to track – not only monocular
cues – but also the actual depth values of the environment. This is especially
useful in augmented reality solutions, where the position and orientation (pose) of
the observer need to be accurately determined. This allows virtual objects to be
installed to the view of the user through, for example, a screen of a tablet or augmented
reality glasses (e.g. Google glass, etc.). Although the early 3D sensors have
been physically quite large, the size of these sensors is decreasing, and possibly –
eventually – a 3D sensor could be embedded – for example – to augmented reality
glasses. The wider subject area considered in this review is 3D SLAM methods,
which take advantage of the 3D information available by modern RGB-D sensors,
such as Microsoft Kinect. Thus the review for SLAM (Simultaneous Localization
and Mapping) and 3D tracking in augmented reality is a timely subject. We also try
to find out the limitations and possibilities of different tracking methods, and how
they should be improved, in order to allow efficient integration of the methods to
the augmented reality solutions of the future.Siirretty Doriast
Monte Carlo Localization in Hand-Drawn Maps
Robot localization is a one of the most important problems in robotics. Most
of the existing approaches assume that the map of the environment is available
beforehand and focus on accurate metrical localization. In this paper, we
address the localization problem when the map of the environment is not present
beforehand, and the robot relies on a hand-drawn map from a non-expert user. We
addressed this problem by expressing the robot pose in the pixel coordinate and
simultaneously estimate a local deformation of the hand-drawn map. Experiments
show that we are able to localize the robot in the correct room with a
robustness up to 80
Recommended from our members
Localization from semantic observations via the matrix permanent
Most approaches to robot localization rely on low-level geometric features such as points, lines, and planes. In this paper, we use object recognition to obtain semantic information from the robot’s sensors and consider the task of localizing the robot within a prior map of landmarks, which are annotated with semantic labels. As object recognition algorithms miss detections and produce false alarms, correct data association between the detections and the landmarks on the map is central to the semantic localization problem. Instead of the traditional vector-based representation, we propose a sensor model, which encodes the semantic observations via random finite sets and enables a unified treatment of missed detections, false alarms, and data association. Our second contribution is to reduce the problem of computing the likelihood of a set-valued observation to the problem of computing a matrix permanent. It is this crucial transformation that allows us to solve the semantic localization problem with a polynomial-time approximation to the set-based Bayes filter. Finally, we address the active semantic localization problem, in which the observer’s trajectory is planned in order to improve the accuracy and efficiency of the localization process. The performance of our approach is demonstrated in simulation and in real environments using deformable-part-model-based object detectors. Robust global localization from semantic observations is demonstrated for a mobile robot, for the Project Tango phone, and on the KITTI visual odometry dataset. Comparisons are made with the traditional lidar-based geometric Monte Carlo localization
- …