Continuum Robot Localization using Distributed Time-of-Flight Sensors

TL;DR

This paper introduces a novel localization method for continuum robots using distributed low-resolution ToF sensors, achieving accurate positioning despite sensor limitations and environmental challenges.

Contribution

The work presents a new localization approach that fuses measurements from small ToF sensors with a shape prior, enabling effective localization in unstructured environments.

Findings

Achieved an average localization error of 2.5cm in position.

Achieved an average rotation error of 7.2 degrees.

Validated results across multiple environments in simulation and real-world.

Abstract

Localization and mapping of an environment are crucial tasks for any robot operating in unstructured environments. Time-of-flight (ToF) sensors (e.g.,~lidar) have proven useful in mobile robotics, where high-resolution sensors can be used for simultaneous localization and mapping. In soft and continuum robotics, however, these high-resolution sensors are too large for practical use. This, combined with the deformable nature of such robots, has resulted in continuum robot (CR) localization and mapping in unstructured environments being a largely untouched area. In this work, we present a localization technique for CRs that relies on small, low-resolution ToF sensors distributed along the length of the robot. By fusing measurement information with a robot shape prior, we show that accurate localization is possible despite each sensor experiencing frequent degenerate scenarios. We achieve an average localization error of 2.5cm in position and 7.2{\deg} in rotation across all experimental conditions with a 53cm long robot. We demonstrate that the results are repeated across multiple environments, in both simulation and real-world experiments, and study robustness in the estimation to deviations in the prior map.