Laser scan matching for self‐localization of a walking robot in man‐made environments

The purpose of this paper is to describe a novel application of the well‐established 2D laser scan‐matching technique for self‐localization of a walking robot. The techniques described in this paper enable a walking robot with a 2D laser scanner to obtain precise maps of man‐made environments, which can be useful in search and reconnaissance missions, e.g. in warehouses, production plants, and other industrial areas.

The presented system combines two scan‐matching algorithms (PSM and PLICP) to deal with low‐quality range data from a compact laser scanner and to provide robust self‐localization in various types of man‐made environments. Data from proprioceptive sensors and simplifying assumptions holding in man‐made environments are exploited to compensate for the varying attitude of the walking robot, particularly in uneven terrain.

The experimental results suggest that neglecting either the poor initial pose guess obtained from the legged odometry, or the varying attitude angles of a walking robot's body, may lead to unacceptable results in self‐localization and scan‐based mapping. It is also demonstrated that using the PSM algorithm to compute the initial pose estimate for the more precise PLICP scan‐matching algorithm improves the results of self‐localization.

So far, the presented self‐localization system was tested in limited‐scale indoor experiments. Experiments with more extended and realistic scenarios are scheduled as further work.

Applying techniques described in this paper, the author was able to obtain the robot pose and precise maps of man‐made environments, which can be useful in USAR and reconnaissance missions, also in warehouses, production plants, and other industrial areas.

The scan‐matching algorithms used in the presented research are not new, the contribution lies in combining them in order to overcome issues specific to a small‐size legged platform, using only common affordable hardware.