Related papers: Indoor Smartphone SLAM with Learned Echoic Locatio…
In this paper, we present an acoustic localization system for multiple devices. In contrast to systems which localise a device relative to one or several anchor points, we focus on the joint localisation of several devices relative to each…
In this paper, we present a new ultra-low power method of indoor localization of smartphones (SM) based on zero-energy-devices (ZEDs) beacons instead of active wireless beacons. Each ZED is equipped with a unique identification number coded…
Simultaneous Localization and Mapping (SLAM) allows mobile robots to navigate without external positioning systems or pre-existing maps. Radar is emerging as a valuable sensing tool, especially in vision-obstructed environments, as it is…
Simultaneous localization and mapping (SLAM) algorithms are essential for the autonomous navigation of mobile robots. With the increasing demand for autonomous systems, it is crucial to evaluate and compare the performance of these…
Smartphone sensors based human activity recognition is attracting increasing interests nowadays with the popularization of smartphones. With the high sampling rates of smartphone sensors, it is a highly long-range temporal recognition…
Precise 6-DoF simultaneous localization and mapping (SLAM) from onboard sensors is critical for wearable devices capturing egocentric data, which exhibits specific challenges, such as a wider diversity of motions and viewpoints, prevalent…
Simultaneous Localization and Mapping (SLAM) techniques play a key role towards long-term autonomy of mobile robots due to the ability to correct localization errors and produce consistent maps of an environment over time. Contrarily to…
In recent years, we have observed a clear trend in the rapid rise of autonomous vehicles, robotics, virtual reality, and augmented reality. The core technology enabling these applications, Simultaneous Localization And Mapping (SLAM),…
The integration of sensing and communication (ISAC) is a cornerstone of 6G, enabling simultaneous environmental awareness and communication. This paper explores radio SLAM (simultaneous localization and mapping) as a key ISAC approach,…
The increasing use of voice assistants and related applications has raised significant concerns about the security of Inertial Measurement Units (IMUs) in smartphones. These devices are vulnerable to acoustic eavesdropping attacks,…
The LiDAR and inertial sensors based localization and mapping are of great significance for Unmanned Ground Vehicle related applications. In this work, we have developed an improved LiDAR-inertial localization and mapping system for…
Existing solutions to visual simultaneous localization and mapping (V-SLAM) assume that errors in feature extraction and matching are independent and identically distributed (i.i.d), but this assumption is known to not be true -- features…
For robots navigating using only a camera, illumination changes in indoor environments can cause re-localization failures during autonomous navigation. In this paper, we present a multi-session visual SLAM approach to create a map made of…
In this paper, we propose an artificial intelligence (AI)-enhanced hybrid simultaneous localization and mapping (SLAM) method that performs Bayesian inference directly on raw radio-frequency (RF) signals while learning an environment model…
We present a fast, scalable, and accurate Simultaneous Localization and Mapping (SLAM) system that represents indoor scenes as a graph of objects. Leveraging the observation that artificial environments are structured and occupied by…
Complementing images with inertial measurements has become one of the most popular approaches to achieve highly accurate and robust real-time camera pose tracking. In this paper, we present a keyframe-based approach to visual-inertial…
Versatile and adaptive semantic understanding would enable autonomous systems to comprehend and interact with their surroundings. Existing fixed-class models limit the adaptability of indoor mobile and assistive autonomous systems. In this…
Simultaneous localization and mapping (SLAM) are essential in numerous robotics applications, such as autonomous navigation. Traditional SLAM approaches infer the metric state of the robot along with a metric map of the environment. While…
We introduce LIDAUS Localization of IoT Device via Anchor UAV SLAM), an infrastructure-free, multi-stage SLAM system that utilizes an Unmanned Aerial Vehicle (UAV) to accurately localize IoT devices in a 3D indoor space where GPS signals…
We present Acoustic Inertial Measurement (AIM), a one-of-a-kind technique for indoor drone localization and tracking. Indoor drone localization and tracking are arguably a crucial, yet unsolved challenge: in GPS-denied environments,…