Building a complete inertial navigation system using the limited quality data provided by current smartphones has been regarded challenging, if not impossible. We present a probabilistic approach for orientation and use-case free inertial odometry, which is based on double-integrating rotated accelerations. Our approach uses a probabilistic approach in fusing the noisy sensor data and learning ...

We propose a method for visual odometry using optical flow with a single omnidirectional (catadioptric) camera. We show how omnidirectional images can be used to perform optical flow, discussing the basis of optical flow and some restrictions needed to it and how unwarp these images. In special we describe how to unwarp omnidirectional images to Bird’s eye view, that correspond to scaled orthog...

Abstract This paper proposes a new online calibration method for the differential-drive mobile robot equipped with laser scanner. Our algorithm can jointly estimate 3-DoF extrinsic parameters of scanner and intrinsic (radii wheel spacing) kinematic model. Applying pre-integration theory initially developed IMU sensor to model, we adopt iterative nonlinear optimization minimize cost derived from...

This article presents FAST-LIO2: a fast, robust, and versatile LiDAR-inertial odometry framework. Building on highly efficient tightly coupled iterated Kalman filter, FAST-LIO2 has two key novelties that allow accurate LiDAR navigation (and mapping). The first one is directly registering raw points to the map subsequently update map, i.e., mapping) without extracting features. enables exploitat...