Abstract:
In the past few years, there has been significant
advancement in localization and mapping using stereo cameras.
Despite the recent successes, reliably generating an accurate
geometric map of a large indoor area using stereo vision still
poses significant challenges due to the accuracy and reliability of
depth information especially with small baselines. Most stereo
vision based applications presented to date have used medium to
large baseline stereo cameras with Gaussian error models. Here
we make an attempt to analyze the significance of errors in small
baseline (usually <0.1m) stereo cameras and the validity of the
Gaussian assumption used in the implementation of Kalman
Filter based SLAM algorithms. Sensor errors are analyzed
through experimentations carried out in the form of a robotic
mapping. Then we show that SLAM solutions based on the
Extended Kalman Filter (EKF) could become inconsistent due to
the nature of the observation models used.
