Localising Faster : Efficient and precise lidar-based robot localisation in large-scale environments

• This paper proposes a novel approach for global localisation of mobile robots in large-scale environments. Our method leverages learning-based localisation and filtering-based localisation, to localise the robot efficiently and precisely through seeding Monte Carlo Localisation (MCL) with a deeplearned distribution. In particular, a fast localisation system rapidly estimates the 6-DOF pose through a deep-probabilistic model (Gaussian Process Regression with a deep kernel), then a precise recursive estimator refines the estimated robot pose according to the geometric alignment. More importantly, the Gaussian method (i.e. deep probabilistic localisation) and nonGaussian method (i.e. MCL) can be integrated naturally via importance sampling. Consequently, the two systems can be integrated seamlessly and mutually benefit from each other. To verify the proposed framework, we provide a case study in large-scale localisation with a 3D lidar sensor. Our experiments on the Michigan NCLT long-term dataset show that the proposed method is able to localise the robot in 1.94 s on average (median of 0.8 s) with precision 0.75 m in a largescale environment of approximately 0.5 km 2.

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Robotteknik och automation (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Robotics (hsv//eng)

Nyckelord

Gaussian processes

learning (artificial intelligence)

mobile robots

Monte Carlo methods

neural nets

optical radar

path planning

recursive estimation

robot vision

SLAM (robots)

precise lidar-based robot localisation

large-scale environments

global localisation

Monte Carlo Localisation

MCL

fast localisation system

deep-probabilistic model

Gaussian process regression

deep kernel

precise recursive estimator

Gaussian method

deep probabilistic localisation

large-scale localisation

largescale environment

time 0.8 s

size 0.75 m

Robots

Neural networks

Three-dimensional displays

Laser radar

Kernel

Computer Science

Datavetenskap

Publikations- och innehållstyp

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