Accurate and robust odometry by fusing monocular visual, inertial, and wheel encoder

Abstract

Tracking the pose of a robot has been gaining importance in the field of Robotics, e.g., paving the way for robot navigation. In recent years, monocular visual-inertial odometry (VIO) is widely used to do the pose estimation due to its good performance and low cost. However, VIO cannot estimate the scale or orientation accurately when robots move along straight lines or circular arcs on the ground. To address the problem, in this paper we take the wheel encoder into account, which can provide us with stable translation information as well as small accumulated errors and momentary slippage errors. By jointly considering the kinematic constraints and the planar moving features, an odometry algorithm tightly coupled with monocular camera, IMU, and wheel encoder is proposed to get robust and accurate pose sensing for mobile robots, which mainly contains three steps. First, we present the wheel encoder preintegration theory and noise propagation formula based on the kinematic mobile robot model, which is the basis of accurate estimation in backend optimization. Second, we adopt a robust initialization method to obtain good initial values of gyroscope bias and visual scale in reality, by making full use of the camera, IMU and wheel encoder measurements. Third, we bound the high computation complexity with a marginalization strategy that conditionally eliminates unnecessary measurements in the sliding window. We implement a prototype and several extensive experiments showing that our system can achieve robust and accurate pose estimation, in terms of the scale, orientation and location, compared with the state-of-the-art.

A Semantic Slam System Based on Visual-Inertial Information and around View Images for Underground Parking Lot

“…In recent years, SLAM technology has shifted towards multi-sensor fusion, progressing from visual-inertial fusion to lidar-inertial fusion and ultimately fusing lidar, visual, inertial sensors, wheel encoders, and GPS data. Notable advancements include VINS-Mono [9], VINS-Fusion [10], and OpenVINS [11] for visual-inertial fusion, LIC-Fusion [12] and VIL-SLAM [13] for lidar-visual-inertial fusion, [14] and [15] for enhanced fusion with wheel encoders, VIWO [16] for sliding-window filtering to fuse multi-modal data, and [17] for introducing wheel encoder pre-integration theory and noise propagation formula, enabling tight integration with sensor data. By amalgamating data from multiple sensors, these approaches significantly enhance simultaneous localization and mapping robustness and accuracy.…”