A Survey on Odometry for Autonomous Navigation Systems

Abstract

The development of a navigation system is one of the major challenges in building a fully autonomous platform. Full autonomy requires a dependable navigation capability not only in a perfect situation with clear GPS signals but also in situations, where the GPS is unreliable. Therefore, selfcontained odometry systems have attracted much attention recently. This paper provides a general and comprehensive overview of the state of the art in the field of self-contained, i.e., GPS denied odometry systems, and identifies the out-coming challenges that demand further research in future. Self-contained odometry methods are categorized into five main types, i.e., wheel, inertial, laser, radar, and visual, where such categorization is based on the type of the sensor data being used for the odometry. Most of the research in the field is focused on analyzing the sensor data exhaustively or partially to extract the vehicle pose. Different combinations and fusions of sensor data in a tightly/loosely coupled manner and with filtering or optimizing fusion method have been investigated. We analyze the advantages and weaknesses of each approach in terms of different evaluation metrics, such as performance, response time, energy efficiency, and accuracy, which can be a useful guideline for researchers and engineers in the field. In the end, some future research challenges in the field are discussed.

Bio-inspired celestial compass yields new opportunities for urban localization

“…These filters are made rotating to scan the polarization pattern around the zenith, each UV-pixel generating N measurements per filter turn. The whole system is fully 3D-printed and open-source 1 . The two polarization units, called UV 0 and UV 1 , are arranged orthogonally to each other, i.e.…”

“…This filtering process has been added in this study to improve the vehicle's heading estimation. 1 https://github.com/JuSquare/AntBot/tree/master/CelestialCompass 2) Use of the Hilbert transform: based on the POL-units' pre-processed signals UV 0 and UV 1 (Eq. 1), the Hilbert transform was computed to determine the AoP.…”

“…Besides, the fast development of autonomous drones and ground vehicles involves new technological challenges in navigation. Localizing these systems benefits from a vast number of methods, mostly relying on civilian global navigation satellite systems (GNSS), inertial measurement units (IMU), radars and cameras [1], [2]. These systems must achieve centimetre precision and robust localization estimates to ensure their suitability for large scale applications like long range urban navigation.…”

Pedestrian Dead Reckoning-Assisted Visual Inertial Odometry Integrity Monitoring

“…In the modern world, people are becoming increasingly dependent on location services. The need to provide accurate indoor location services is becoming more and more urgent [1][2][3][4][5]. Indoor positioning technologies based on various types of sensors, such as Wi-Fi [6], Bluetooth [7], cameras [8], and inertial sensors [9], are rapidly developing.…”

“…Vision-based indoor positioning technology can be divided into visual odometry (VO) [5] and visual-inertial odometry (VIO) [11]. The VO estimates the motion of a camera based on the movement of features in the captured image.…”