Monocular Visual-Inertial Odometry with an Unbiased Linear System Model and Robust Feature Tracking Front-End

Abstract

The research field of visual-inertial odometry has entered a mature stage in recent years. However, unneglectable problems still exist. Tradeoffs have to be made between high accuracy and low computation for users. In addition, notation confusion exists in quaternion descriptions of rotation; although not fatal, this may results in unnecessary difficulties in understanding for researchers. In this paper, we develop a visual-inertial odometry which gives consideration to both precision and computation. The proposed algorithm is a filter-based solution that utilizes the framework of the noted multi-state constraint Kalman filter. To dispel notation confusion, we deduced the error state transition equation from scratch, using the more cognitive Hamilton notation of quaternion. We further come up with a fully linear closed-form formulation that is readily implemented. As the filter-based back-end is vulnerable to feature matching outliers, a descriptor-assisted optical flow tracking front-end was developed to cope with the issue. This modification only requires negligible additional computation. In addition, an initialization procedure is implemented, which automatically selects static data to initialize the filter state. Evaluations of proposed methods were done on a public, real-world dataset, and comparisons were made with state-of-the-art solutions. The experimental results show that the proposed solution is comparable in precision and demonstrates higher computation efficiency compared to the state-of-the-art.

From Pixels to Precision: A Survey of Monocular Visual Odometry in Digital Twin Applications

“…An uncertainty assessment is essential for evaluating the reliability of the estimated camera trajectory in VO. While traditional VO approaches often provide an analytical formula for uncertainty, this remains an open challenge for machine learning-based VO methods [ 40 ].…”

Consistent Monocular Ackermann Visual–Inertial Odometry for Intelligent and Connected Vehicle Localization

“…Considering the fact that the measurements of vehicle kinematic error originate from the vehicle velocity, angular rate, and the characteristics of the vehicle relative kinematic errors between two consecutive camera states, the method using a kinematic error measurement model for the vehicle has different parameter configurations in practical application. Therefore, following Equation (15), the tunable parameter configurations of ACK-MSCKF are as shown in Table 1. The limitation of the above relative kinematic error measurement model for the vehicle derives from the assumption that the vehicle velocity is constant for low-speed motion between two consecutive camera states.…”

“…It is widely used in the pose estimation of mobile robots owing to the advantages of favorable robustness, small size and low cost. VIO mainly consists of filter-based methods, including the Multi-Sensor Fusion Approach (MSF) [ 8 ], the Robust Visual-Inertial Odometry (ROVIO) [ 9 ], the Multi-State Constraint Kalman Filter (MSCKF) [ 10 ], Stereo-MSCKF [ 11 ], S-MSCKF [ 12 ], the Robocentric Visual-Inertial Odometry (R-VIO) [ 13 ], Schmidt-MSCKF [ 14 ], the Lightweight Hybrid Visual-Inertial Odometry (LARVIO) [ 15 , 16 ] and optimization-based methods including the Open Keyframe-based Visual-Inertial SLAM (OKVIS) [ 17 ], ORB-SLAM-VI [ 18 ], VINS-Mono [ 19 ], PL-VIO [ 20 ], ICE-BA [ 21 ], VI-DSO [ 22 ], VINS-Fusion [ 23 , 24 ], Basalt [ 25 ] and ORB-SLAM3 [ 26 ]. A detailed review of the VIO methods can be found in the literature [ 27 , 28 ].…”

A Hybrid Sliding Window Optimizer for Tightly-Coupled Vision-Aided Inertial Navigation System

“…In this study, we focused on estimating a vehicle’s motion by fusing the measurements from a monocular camera and an inertial measurement unit (IMU). This task—the well-known monocular vision-aided inertial navigation system (VINS) problem—has drawn great interest in the robotic community (e.g., [1,2,3,4,5,6,7]) for many reasons.…”