Robust Stereo Visual Inertial Navigation System Based on Multi-Stage Outlier Removal in Dynamic Environments

Abstract

Robotic mapping and odometry are the primary competencies of a navigation system for an autonomous mobile robot. However, the state estimation of the robot typically mixes with a drift over time, and its accuracy is degraded critically when using only proprioceptive sensors in indoor environments. Besides, the accuracy of an ego-motion estimated state is severely diminished in dynamic environments because of the influences of both the dynamic objects and light reflection. To this end, the multi-sensor fusion technique is employed to bound the navigation error by adopting the complementary nature of the Inertial Measurement Unit (IMU) and the bearing information of the camera. In this paper, we propose a robust tightly-coupled Visual-Inertial Navigation System (VINS) based on multi-stage outlier removal using the Multi-State Constraint Kalman Filter (MSCKF) framework. First, an efficient and lightweight VINS algorithm is developed for the robust state estimation of a mobile robot by practicing a stereo camera and an IMU towards dynamic indoor environments. Furthermore, we propose strategies to deal with the impacts of dynamic objects by using multi-stage outlier removal based on the feedback information of estimated states. The proposed VINS is implemented and validated through public datasets. In addition, we develop a sensor system and evaluate the VINS algorithm in the dynamic indoor environment with different scenarios. The experimental results show better performance in terms of robustness and accuracy with low computation complexity as compared to state-of-the-art approaches.

General Moving Object Localization from a Single Flying Camera

“…Grewe and Stevenson [18] used a drone-based stereo camera system to assist a low-vision person with environment awareness by detecting a user's heading and direction. Nam and Gun-Woo [19] showed the possibility of using stereo vision with the Inertial Measurement Unit (IMU) to form a tightly coupled Visual-Inertial Navigation System (VINS), which shows a better performance in terms of robustness and accuracy. There is no restriction on the number of cameras for the stereo vision method.…”

“…By simplifying Equations (18) and (19), we can isolate the y-axis intercept "b" as an expression using the slope "a" from Equation (3). From Equations (18) and (19), we can get b 1 and b 2 , respectively, as follows:…”

VIMO: A Visual-Inertial-Magnetic Navigation System Based on Non-Linear Optimization

“…Copious studies have been done on visual-inertial SLAM along with various applications that have since been on a continuum to full maturity [9][10][11]. Until today, scholars are still endeavoring to bring it to perfection with novel ideas and approaches [12][13][14][15][16]. Incipient methods fuse inertial and visual measurements loosely where the two types of information are processed, filtered, and used for estimation all separately [17].…”

VINS-Dimc: A Visual-Inertial Navigation System for Dynamic Environment Integrating Multiple Constraints

“…Their approach combines geometric information and semantic segmentation information to track keypoints. In [23], Nam et al propose a robust tightly-coupled VINS that uses the multi-stage outlier removal method. It uses the multi-stage outlier removal method to deal with the influence of moving objects based on the feedback information of the estimated states.…”