Abstract
We present a graph SLAM system based on Hidden Markov Models (HMM) where the sensor readings are represented with different symbols using a number of clustering techniques; then, the symbols are fused as a single prediction, to improve the accuracy rate, using a Dual HMM. Our system’s versatility allows to work with different types of sensors or fusion of sensors, and to implement, either active or passive, graph SLAM. The Toyota HSR (Human Support Robot) robot was used to generate the data set in both real and simulated competition environments. We tested our system in the kidnapped robot problem by training a representation, improving it online, and, finally, solving the SLAM problem.
Highlights
Service robots, such as the Toyota HSR [1], are increasingly becoming a part of our everyday life, so the ability to explore, map, and navigate its surroundings is of the utmost importance
A robust simultaneous localization and mapping (SLAM) system should benefit from all symbol representations; Sensor Fusion [24] is used in this way, yet we propose a much simpler approach: Fig. 3
We present results using a Gazebo simulated home environment, a typical indoor setting for service robots [27]; even though a map is provided with the simulated environments, we do not use it in our SLAM tests, except for Figure clarity
Summary
Service robots, such as the Toyota HSR [1], are increasingly becoming a part of our everyday life, so the ability to explore, map, and navigate its surroundings is of the utmost importance. The Rao-Blackwellized particle filter for SLAM makes use of the following factorization: P(x1:t , m | z1:t , u1:t−1) = P(m | x1:t , z1:t )P(x1:t | z1:t , u1:−t−1), where x is the pose of the robot, m is the map, Zt are the observations at time t, and Ut is the control signal. Model training is done with a labeled training set This training set is formed by a vector O⃗ t that contains all the 720 laser readings from a laser sensor and an odometry-based pose vector or hidden state Sk. Baum-Welch [15] is the most commonly used method for the HMM model estimation.
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