A Low-Cost Consistent Vehicle Localization Based on Interval Constraint Propagation

Zhan Wang,Alain Lambert

doi:10.1155/2018/2713729

Abstract

Probabilistic techniques (such as Extended Kalman Filter and Particle Filter) have long been used to solve robotic localization and mapping problem. Despite their good performance in practical applications, they could suffer inconsistency problems. This paper proposes an interval analysis based method to estimate the vehicle pose (position and orientation) in a consistent way, by fusing low-cost sensors and map data. We cast the localization problem into an Interval Constraint Satisfaction Problem (ICSP), solved via Interval Constraint Propagation (ICP) techniques. An interval map is built when a vehicle embedding expensive sensors navigates around the environment. Then vehicles with low-cost sensors (dead reckoning and monocular camera) can use this map for ego-localization. Experimental results show the soundness of the proposed method in achieving consistent localization.

Highlights

Using sensory information to locate the robot in its environment is one of the most fundamental problem providing a mobile robot with autonomous capabilities
A simulation experiment is set up to evaluate the performance of our proposed Interval Constraint Satisfaction Problem (ICSP) based mapping and localization method
root of sum square error (RSSE) 8.90 8.29 11.8 visual learning step to reconstruct a map of the environment, and they use this map for localization and navigation task

Summary

Introduction

Using sensory information to locate the robot in its environment is one of the most fundamental problem providing a mobile robot with autonomous capabilities. A vast number of works [1, 2] dedicated to such problems use both proprioceptive and exteroceptive sensors Proprioceptive sensors such as odometers and gyrometers are used to calculate the elementary movements, which are used to estimate the robot pose (position and orientation). This generates cumulative errors as the robot moves [3, 4]. The most commonly used methods are Kalman filtering and Particle filtering [7, 8] These methods associate a probability to a position, but nothing ensures that the robot is in the position with the highest probability. In real world, many sensors do not possess Gaussian noise model or their distributions are not available [10]

Results

Discussion

Conclusion