A Systematic Evaluation of Multi-Sensor Array Configurations for SLAM Tracking with Agile Movements

Accurate tracking of a user in a marker-less environment can be difficult, even more so when agile head or hand movements are expected. When relying on feature detection as part of a SLAM algorithm the issue arises that a large rotational delta causes previously tracked features to become lost. One approach to overcome this problem is with multiple sensors increasing the horizontal field of view. In this paper, we perform a systematic evaluation of tracking accuracy by recording several agile movements and providing different camera configurations to evaluate against. We begin with four sensors in a square configuration and test the resulting output from a chosen SLAM algorithm. We then systematically remove a camera from the feed covering all permutations to determine the level of accuracy and tracking loss. We cover some of the lessons learned in this preliminary experiment and how it may guide researchers in tracking extremely agile movements.