In this paper, we present a novel method to fuse observations from an inertial measurement unit (IMU) and visual sensors, such that initial conditions of the inertial integration, including gravity estimation, can be recovered quickly and in a linear manner, thus removing any need for special initialization procedures. The algorithm is implemented using a graphical simultaneous localization and mapping like approach that guarantees constant time output. This paper discusses the technical aspects of the work, including observability and the ability for the system to estimate scale in real time. Results are presented of the system, estimating the platforms position, velocity, and attitude, as well as gravity vector and sensor alignment and calibration on-line in a built environment. This paper discusses the system setup, describing the real-time integration of the IMU data with either stereo or monocular vision data. We focus on human motion for the purposes of emulating high-dynamic motion, as well as to provide a localization system for future human-robot interaction.

The use of accelerometer and gyro observations in a visual SLAM implementation is beneficial especially in high dynamic situations. The downside of using inertial is that traditionally high prediction rates are required as observations are provided at high sample rates. An accurate orientation and velocity estimate must also be maintained at all times in order to integrate the inertial observations and correct for the effect of gravity. This paper presents a way to pre-integrate the high rate inertial observations without the need for an initial orientation or velocity estimate. This allows for a slower filter prediction rate and use of inertial observations when the initial velocity and attitude of the platform are unknown. Additionally the initial velocity and roll and pitch of the platform become observable over time and an estimate of these values is provided by the filter. An estimate of the gravity vector is also provided. Results are presented using a delayed state information smoother implementation however due to the linearity of the equations this technique can be applied to extended Kalman filter (EKF) implementations just as easily.

This paper identifies various scale factor biases commonly introduced into monocular SLAM implementations as a result of the true scale factor of the map not being observable. A way to make the scale factor observable and remove any scale biases via the use of an inertial measurement unit (IMU) is presented and implemented. Results show that with an IMU the true scale of the map becomes observable over time and the use of a square root information filter allows the effect of initial scale biases to be removed completely from the solution resulting in an unbiased solution no matter what the initial scale assumptions are.

This paper presents the benefits of using a low cost inertial measurement unit to aid in an implementation of inverse depth initialized SLAM using a hand-held monocular camera. Results are presented with and without inertial observations for different assumed initial ranges to features on the same dataset. When using only the camera, the scale of the scene is not observable. As expected, the scale of the map depends on the prior used to initialize the depth of the features and may drift when exploring new terrain, precluding loop closure. The results show that the inertial observations help to improve the estimated trajectory of the camera leading to a better estimation of the map scale and a more accurate localization of features.