Visual-Inertial Sensor Fusion: Localization, Mapping and Sensor-to-Sensor Self-calibration

被引:404
作者
Kelly, Jonathan [1 ]
Sukhatme, Gaurav S. [1 ]
机构
[1] Univ So Calif, Robot Embedded Syst Lab, Los Angeles, CA 90089 USA
基金
美国国家科学基金会;
关键词
Self-calibration; sensor fusion; observability; computer vision; inertial navigation; RELATIVE POSE CALIBRATION; OBSERVABILITY ANALYSIS; KALMAN FILTER; VISION; CAMERA; NAVIGATION; MOTION;
D O I
10.1177/0278364910382802
中图分类号
TP24 [机器人技术];
学科分类号
080202 ; 1405 ;
摘要
Visual and inertial sensors, in combination, are able to provide accurate motion estimates and are well suited for use in many robot navigation tasks. However, correct data fusion, and hence overall performance, depends on careful calibration of the rigid body transform between the sensors. Obtaining this calibration information is typically difficult and time-consuming, and normally requires additional equipment. In this paper we describe an algorithm, based on the unscented Kalman filter, for self-calibration of the transform between a camera and an inertial measurement unit (IMU). Our formulation rests on a differential geometric analysis of the observability of the camera-IMU system; this analysis shows that the sensor-to-sensor transform, the IMU gyroscope and accelerometer biases, the local gravity vector, and the metric scene structure can be recovered from camera and IMU measurements alone. While calibrating the transform we simultaneously localize the IMU and build a map of the surroundings, all without additional hardware or prior knowledge about the environment in which a robot is operating. We present results from simulation studies and from experiments with a monocular camera and a low-cost IMU, which demonstrate accurate estimation of both the calibration parameters and the local scene structure.
引用
收藏
页码:56 / 79
页数:24
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