372 research outputs found
A Rao-Blackwellized Particle Filter for Topological Mapping
©2006 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Presented at the 2006 IEEE International Conference on Robotics and Automation (ICRA), 15-19 May 2006, Orlanda, FL.DOI: 10.1109/ROBOT.2006.1641809We present a particle filtering algorithm to construct
topological maps of an uninstrument environment. The
algorithm presented here constructs the posterior on the space
of all possible topologies given measurements, and is based on our
previous work on a Bayesian inference framework for topological
maps [21]. Constructing the posterior solves the perceptual
aliasing problem in a general, robust manner. The use of a
Rao-Blackwellized Particle Filter (RBPF) for this purpose makes
the inference in the space of topologies incremental and run in
real-time. The RBPF maintains the joint posterior on topological
maps and locations of landmarks. We demonstrate that, using the
landmark locations thus obtained, the global metric map can be
obtained from the topological map generated by our algorithm
through a simple post-processing step. A data-driven proposal
is provided to overcome the degeneracy problem inherent in
particle filters. The use of a Dirichlet process prior on landmark
labels is also a novel aspect of this work. We use laser range scan
and odometry measurements to present experimental results on
a robot
Efficiently learning metric and topological maps with autonomous service robots
Models of the environment are needed for a wide range of robotic applications, from search and rescue to automated vacuum cleaning. Learning maps has therefore been a major research focus in the robotics community over the last decades. In general, one distinguishes between metric and topological maps. Metric maps model the environment based on grids or geometric representations whereas topological maps model the structure of the environment using a graph. The contribution of this paper is an approach that learns a metric as well as a topological map based on laser range data obtained with a mobile robot. Our approach consists of two steps. First, the robot solves the simultaneous localization and mapping problem using an efficient probabilistic filtering technique. In a second step, it acquires semantic information about the environment using machine learning techniques. This semantic information allows the robot to distinguish between different types of places like, e. g., corridors or rooms. This enables the robot to construct annotated metric as well as topological maps of the environment. All techniques have been implemented and thoroughly tested using real mobile robot in a variety of environments
Bayesian Train Localization with Particle Filter, Loosely Coupled GNSS, IMU, and a Track Map
Train localization is safety-critical and therefore the approach requires a continuous availability and a track-selective accuracy.
A probabilistic approach is followed up in order to cope with multiple sensors, measurement errors, imprecise information, and
hidden variables as the topological position within the track network. The nonlinear estimation of the train localization posterior is
addressed with a novel Rao-Blackwellized particle filter (RBPF) approach. There, embedded Kalman filters estimate certain linear
state variables while the particle distribution can cope with the nonlinear cases of parallel tracks and switch scenarios. The train
localization algorithmis further based on a trackmap andmeasurements froma GlobalNavigation Satellite System(GNSS) receiver
and an inertial measurement unit (IMU). The GNSS integration is loosely coupled and the IMU integration is achieved without
the common strapdown approach and suitable for low-cost IMUs.The implementation is evaluated with realmeasurements from a
regional train at regular passenger service over 230 km of tracks with 107 split switches and parallel track scenarios of 58.5 km.The
approach is analyzed with labeled data by means of ground truth of the traveled switch way. Track selectivity results reach 99.3%
over parallel track scenarios and 97.2% of correctly resolved switch ways
3D Reconstruction & Assessment Framework based on affordable 2D Lidar
Lidar is extensively used in the industry and mass-market. Due to its
measurement accuracy and insensitivity to illumination compared to cameras, It
is applied onto a broad range of applications, like geodetic engineering, self
driving cars or virtual reality. But the 3D Lidar with multi-beam is very
expensive, and the massive measurements data can not be fully leveraged on some
constrained platforms. The purpose of this paper is to explore the possibility
of using cheap 2D Lidar off-the-shelf, to preform complex 3D Reconstruction,
moreover, the generated 3D map quality is evaluated by our proposed metrics at
the end. The 3D map is constructed in two ways, one way in which the scan is
performed at known positions with an external rotary axis at another plane. The
other way, in which the 2D Lidar for mapping and another 2D Lidar for
localization are placed on a trolley, the trolley is pushed on the ground
arbitrarily. The generated maps by different approaches are converted to
octomaps uniformly before the evaluation. The similarity and difference between
two maps will be evaluated by the proposed metrics thoroughly. The whole
mapping system is composed of several modular components. A 3D bracket was made
for assembling of the Lidar with a long range, the driver and the motor
together. A cover platform made for the IMU and 2D Lidar with a shorter range
but high accuracy. The software is stacked up in different ROS packages.Comment: 7 pages, 9 Postscript figures. Accepted by 2018 IEEE International
Conference on Advanced Intelligent Mechatronic
Long-term experiments with an adaptive spherical view representation for navigation in changing environments
Real-world environments such as houses and offices change over time, meaning that a mobile robot’s map will become out of date. In this work, we introduce a method to update the reference views in a hybrid metric-topological map so that a mobile robot can continue to localize itself in a changing environment. The updating mechanism, based on the multi-store model of human memory, incorporates a spherical metric representation of the observed visual features for each node in the map, which enables the robot to estimate its heading and navigate using multi-view geometry, as well as representing the local 3D geometry of the environment. A series of experiments demonstrate the persistence performance of the proposed system in real changing environments, including analysis of the long-term stability
Extending the Occupancy Grid Concept for Low-Cost Sensor Based SLAM
The simultaneous localization and mapping problem is approached by using an ultrasound sensor and wheel encoders. To be able to account for the low precision inherent in ultrasound sensors, the occupancy grid notion is extended. The extension takes into consideration with which angle the sensor is pointing, to compensate for the issue that an object is not necessarily detectable from all position due to deficiencies in how ultrasonic range sensors work. Also, a mixed linear/nonlinear model is derived for future use in Rao-Blackwellized particle smoothing
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