Mixed-Degree Cubature H∞ Information Filter-Based Visual-Inertial Odometry

Abstract

Visual–inertial odometry is an effective system for mobile robot navigation. This article presents an egomotion estimation method for a dual-sensor system consisting of a camera and an inertial measurement unit (IMU) based on the cubature information filter and H∞ filter. The intensity of the image was used as the measurement directly. The measurements from the two sensors were fused with a hybrid information filter in a tightly coupled way. The hybrid filter used the third-degree spherical-radial cubature rule in the time-update phase and the fifth-degree spherical simplex-radial cubature rule in the measurement-update phase for numerical stability. The robust H∞ filter was combined into the measurement-update phase of the cubature information filter framework for robustness toward non-Gaussian noises in the intensity measurements. The algorithm was evaluated on a common public dataset and compared to other visual navigation systems in terms of absolute and relative accuracy.

Special Issue on Mobile Robots Navigation

“…This problem is commonly known as visual odometry (VO). In this regard, the work by Song et al [5] presents a Visual-Inertial Odometry approach, based on the cubature information filter and H ∞ filter, namely, MCH ∞ IF − V IO, which uses a raw intensity-based measurement model for the camera. On the one hand, the measurements from the IMU (Inertial Measurement Unit), and the camera are fused by means of a hybrid information filter, which applies two cubature rules in the time-update and the measurement-update phases, to guarantee numerical stability.…”

Brain-Like Navigation Scheme based on MEMS-INS and Place Recognition

“…However, in GPS blocked environments, the GPS/INS system would be shut down and go back to INS standalone mode and the navigation error would be accumulated [14]. As a common integrated navigation method for UAVs, visual navigation also has many drawbacks [15]. On the one hand, the performance of visual navigation is inversely proportional to the velocity.…”