1,564 research outputs found
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
Implicit sampling for path integral control, Monte Carlo localization, and SLAM
The applicability and usefulness of implicit sampling in stochastic optimal
control, stochastic localization, and simultaneous localization and mapping
(SLAM), is explored; implicit sampling is a recently-developed
variationally-enhanced sampling method. The theory is illustrated with
examples, and it is found that implicit sampling is significantly more
efficient than current Monte Carlo methods in test problems for all three
applications
Reliable Monte Carlo Localization for Mobile Robots
Reliability is a key factor for realizing safety guarantee of full autonomous
robot systems. In this paper, we focus on reliability in mobile robot
localization. Monte Carlo localization (MCL) is widely used for mobile robot
localization. However, it is still difficult to guarantee its safety because
there are no methods determining reliability for MCL estimate. This paper
presents a novel localization framework that enables robust localization,
reliability estimation, and quick re-localization, simultaneously. The
presented method can be implemented using similar estimation manner to that of
MCL. The method can increase localization robustness to environment changes by
estimating known and unknown obstacles while performing localization; however,
localization failure of course occurs by unanticipated errors. The method also
includes a reliability estimation function that enables us to know whether
localization has failed. Additionally, the method can seamlessly integrate a
global localization method via importance sampling. Consequently, quick
re-localization from failures can be realized while mitigating noisy influence
of global localization. Through three types of experiments, we show that
reliable MCL that performs robust localization, self-failure detection, and
quick failure recovery can be realized
Mobile robot localization failure recovery
Mobile robot localization is one of the most important problems in robotics. Localization is the process of a robot finding out its location given a map of its environment. A number of successful localization solutions have been proposed, among them the well-known and popular Monte Carlo localization method, which is based on particle filters. This thesis proposes a localization approach based on particle filters, using a different way of initializing and resampling of the particles, that reduces the cost of localization. Ultrasonic and light sensors are used in order to perform the experiments. Monte Carlo Localization may fail to localize the robot properly because of the premature convergence of the particles. Using more number of particles increases the computational cost of localization process. Experimental results show that, applying the proposed method robot can successfully localize itself using less number of particles; therefore the cost of localization is decreased
Monte Carlo localization for teach-and-repeat feature-based navigation
This work presents a combination of a teach-and-replay visual navigation and Monte Carlo localization methods. It improves a reliable teach-and-replay navigation method by replacing its dependency on precise dead-reckoning by introducing Monte Carlo localization to determine robot position along the learned path. In consequence, the navigation method becomes robust to dead-reckoning errors, can be started from at any point in the map and can deal with the `kidnapped robot' problem. Furthermore, the robot is localized with MCL only along the taught path, i.e. in one dimension, which does not require a high number of particles and significantly reduces the computational cost.
Thus, the combination of MCL and teach-and-replay navigation mitigates the disadvantages of both methods. The method was tested using a P3-AT ground robot and a Parrot AR.Drone aerial robot over a long indoor corridor. Experiments show the validity of the approach and establish a solid base for continuing this work
Monte Carlo localization algorithm based on particle swarm optimization
In wireless sensor networks, Monte Carlo localization for mobile nodes has a large positioning error and slow convergence speed. To address the challenges of low sampling efficiency and particle impoverishment, a time sequence Monte Carlo localization algorithm based on particle swarm optimization (TSMCL-BPSO) is proposed in this paper. Firstly, the sampling region is constructed according to the overlap of the initial sampling region and the Monte Carlo sampling region. Then, particle swarm optimization (PSO) strategy is adopted to search the optimum position of the target node. The velocity of particle swarm is updated by adaptive step size and the particle impoverishment is improved by distributed estimation and particle replication, which avoids the local optimum caused by the premature convergence of particles. Experiment results indicate that the proposed algorithm improves the particle fitness, increases the particle searching efficiency, and meanwhile the lower positioning error can be obtained at the node\u27s maximum speed of 70âm/s
INCORPORATING HISTOGRAMS OF ORIENTED GRADIENTS INTO MONTE CARLO LOCALIZATION
This work presents improvements to Monte Carlo Localization (MCL) for a mobile robot using computer vision. Solutions to the localization problem aim to provide fine resolution on location approximation, and also be resistant to changes in the environment. One such environment change is the kidnapped/teleported robot problem, where a robot is suddenly transported to a new location and must re-localize. The standard method of Augmented MCL uses particle filtering combined with addition of random particles under certain conditions to solve the kidnapped robot problem. This solution is robust, but not always fast. This work combines Histogram of Oriented Gradients (HOG) computer vision with particle filtering to speed up the localization process.
The major slowdown in Augmented MCL is the conditional addition of random particles, which depends on the ratio of a short term and long term average of particle weights. This ratio does not change quickly when a robot is kidnapped, leading the robot to believe it is in the wrong location for a period of time. This work replaces this average-based conditional with a comparison of the HOG image directly in front of the robot with a cached version. This resulted in a speedup ranging from from 25.3% to 80.7% (depending on parameters used) in localization time over the baseline Augmented MCL
Loc-NeRF: Monte Carlo Localization using Neural Radiance Fields
We present Loc-NeRF, a real-time vision-based robot localization approach
that combines Monte Carlo localization and Neural Radiance Fields (NeRF). Our
system uses a pre-trained NeRF model as the map of an environment and can
localize itself in real-time using an RGB camera as the only exteroceptive
sensor onboard the robot. While neural radiance fields have seen significant
applications for visual rendering in computer vision and graphics, they have
found limited use in robotics. Existing approaches for NeRF-based localization
require both a good initial pose guess and significant computation, making them
impractical for real-time robotics applications. By using Monte Carlo
localization as a workhorse to estimate poses using a NeRF map model, Loc-NeRF
is able to perform localization faster than the state of the art and without
relying on an initial pose estimate. In addition to testing on synthetic data,
we also run our system using real data collected by a Clearpath Jackal UGV and
demonstrate for the first time the ability to perform real-time global
localization with neural radiance fields. We make our code publicly available
at https://github.com/MIT-SPARK/Loc-NeRF
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