top of page
파란 차

Radio SLAM

1. mmWave/THz Radio SLAM

Fig. 1.png

Millimeter wave (mmWave) signals in fifth-generation (5G) communications can enable accurate positioning for vehicular networks. However, radio-based positioning suffers from multipath signals generated by various objects in the physical environment. Multipaths can be leveraged as a benefit by building a radio map, which comprises the number of objects, object type, and object state. The radio map is utilized to optimize all available signal paths for positioning and mapping, serving as a solution to radio simultaneous localization and mapping (SLAM).

Fig. 1.  Radio SLAM scenario with the base station, the vehicle, and four virtual anchors (each corresponding to a vertical wall).

<5G mmWave radio SLAM simulation>

Fig. 2.avif

Fig. 2. 5G mmWave cooperative radio SLAM

Fig. 2 illustrates the radio SLAM scenario, where 5G vehicular networks can be utilized to share measurements, radio maps, and location information, thereby enabling the cooperative positioning and mapping. In 5G mmWave cooperative positioning and mapping, three main tasks are as follows:(i) Vehicle positioning: determine the states (position, velocity, heading, and clock bias) of the vehicles;(ii) Environment mapping: estimate the number of objects, as well as each object’s type and location;and (iii) Cooperation: fuse the collected map from vehicles and transmit it to each vehicle. As shown in Fig. 3 below, the mapping of landmarks is achieved quickly by the cooperation between the base station and vehicles.

Fig. 3. Mapping accuracy without the cooperation (left) and with the cooperation (right)

Fig. 4.png

Fig. 4.  Radio signal propagation by arbitrarily-shaped objects and flat walls

Most existing radio SLAM works focus on point-like landmarks, such as virtual anchors and scattering points, which can be represented using fixed 3D coordinates. However, real-world environments, particularly in indoor or urban scenarios, contain many irregular reflectors, as shown in Fig. 4. For correctly classifying various types of landmarks, we proposed a novel approach that maps arbitrarily shaped reflectors using a Dirichlet process clustering-based radio SLAM. The proposed technique can detect the types of multiple objects, classify each object to perform the mapping, and utilize this mapping information to improve the positioning accuracy.

Fig_edited_edited.jpg

Fig. 5. Environment mapping procedure for (a) Active sensing, (b) Passive sensing, (c) Landmark estimation

Conventional radio SLAM frameworks assume a single dominant type of wall reflection, either specular or diffuse, ignoring a mixture of reflection types. Furthermore, single-mode radio SLAM approaches fail to robustly detect or map walls in environments with mixed reflection characteristics. To detect both mirror-like and rough walls robustly, we proposed a hybrid-sensing radio SLAM framework that combines: (i) Active sensing, which is effective for the detection of rough walls; (ii) Passive sensing, which complements mirror-like walls. As shown in Fig. 6, the proposed framework outperforms both active-only and passive-only SLAM in environments with mixed reflection surfaces by integrating data from both sensing modalities into a unified map.

Fig. 6.png

Fig. 6. Mapping performance by the proposed method compared to radio SLAM with only active or passive sensing

2. AI/ML based radio SLAM

In radio SLAM, reducing information loss and computational complexity is a critical challenge to enable accurate and real-time mapping. As shown in Fig. 7, we propose a machine learning-driven landmark mapping algorithm using channel impulse responses (CIRs). The algorithm leverages CIRs, which preserve the raw characteristics of radio propagation, to enhance mapping performance. Furthermore, it replaces mapping filters in existing radio SLAM algorithms with machine learning to reduce computational complexity and improve efficiency.

Fig. 7.png

Fig. 7.  Example of machine learning-driven landmark mapping using CIRs

김선우 교수

한양대학교 융합전자공학부

서울특별시 성동구 왕십리로 한양대학교, 04763

교수연구실: IT/BT관 817호 T) +82-2-2220-4823

학생연구실: 퓨전테크센터 516호

​행정실: IT/BT관 822호 T) +82-2-2220-4822

Professor Sunwoo Kim

Dept. of Electronic Engineering, Hanyang University

222 Wangsimri-ro Seongdong-gu Seoul Korea, 04763

T) +82-2-2220-4822

04 좌우조합형_영문 1도-01.png
LOGO_GRAY-01.png
bottom of page