Abstract
A new scan that matches an aided Inertial Navigation System (INS) with a low-cost LiDAR is proposed as an alternative to GNSS-based navigation systems in GNSS-degraded or -denied environments such as indoor areas, dense forests, or urban canyons. In these areas, INS-based Dead Reckoning (DR) and Simultaneous Localization and Mapping (SLAM) technologies are normally used to estimate positions as separate tools. However, there are critical implementation problems with each standalone system. The drift errors of velocity, position, and heading angles in an INS will accumulate over time, and on-line calibration is a must for sustaining positioning accuracy. SLAM performance is poor in featureless environments where the matching errors can significantly increase. Each standalone positioning method cannot offer a sustainable navigation solution with acceptable accuracy. This paper integrates two complementary technologies—INS and LiDAR SLAM—into one navigation frame with a loosely coupled Extended Kalman Filter (EKF) to use the advantages and overcome the drawbacks of each system to establish a stable long-term navigation process. Static and dynamic field tests were carried out with a self-developed Unmanned Ground Vehicle (UGV) platform—NAVIS. The results prove that the proposed approach can provide positioning accuracy at the centimetre level for long-term operations, even in a featureless indoor environment.
Highlights
High-precision dynamic positioning is in great demand in Unmanned Aerial Vehicle (UAV) and Unmanned Ground Vehicle (UGV) applications
A software platform programmed with C++ and Qt was designed for recording the raw data and post-processing navigation; Figure 5 shows the Graphic User Interface (GUI) of the
This paper proposes a Light Detection and Ranging (LiDAR) scan-matching aided inertial navigation system based on a commercial-grade Inertial Measurement Units (IMU) and LiDAR combined into one system, with raw IMU outputs used to refine the search scope of Simultaneous Localization and Mapping (SLAM) to optimize brute search efficiency
Summary
High-precision dynamic positioning is in great demand in Unmanned Aerial Vehicle (UAV) and Unmanned Ground Vehicle (UGV) applications. Though SLAM systems focus on localization and mapping, the essence of feature matching in SLAM is identical to visually based navigation systems This passive sensing solution extensively relies on the lighting situation of the detected environment, which restricts its applications. Carried out pilot research by proposing a LiDAR scan matching method with a Gauss-Newton algorithm; the matching results were fused with the measurements of an IMU to estimate a full 3D-motion of a moving platform. A non-feature extracted-grid map-based global scan matching algorithm is applied to aid the inertial system It is more accurate and stable while providing low computational complexity. LiDAR scan matching depends heavily on environmental features, and IMUs can assist system navigation in a featureless “outage” environment for a short period to sustain a highly accurate positioning solution until geometric features are detected to aid the inertial system. The rest of this paper is organized as follows: Section 2 describes the system workflow and error models of the INS and LiDAR; Section 3 discusses the field tests and experimental results; and Section 4 offers conclusions
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