Related papers: NightOwl: Robotic Platform for Wheeled Service Rob…
This paper implements Simultaneous Localization and Mapping (SLAM) technique to construct a map of a given environment. A Real Time Appearance Based Mapping (RTAB-Map) approach was taken for accomplishing this task. Initially, a 2d…
Simultaneous Localization and Mapping (SLAM) is considered to be an essential capability for intelligent vehicles and mobile robots. However, most of the current lidar SLAM approaches are based on the assumption of a static environment.…
Simultaneous Localization and Mapping (SLAM) enables autonomous robots to navigate and execute their tasks through unknown environments. However, performing SLAM in large environments with a single robot is not efficient, and visual or…
This paper presents a method for designing energy-aware collaboration tasks between humans and robots, and generating corresponding trajectories to carry out those tasks. The method involves using high-level specifications expressed as…
Combining lidar in camera-based simultaneous localization and mapping (SLAM) is an effective method in improving overall accuracy, especially at a large scale outdoor scenario. Recent development of low-cost lidars (e.g. Livox lidar) enable…
In this paper, we propose an interoceptive-only odometry system for ground robots with neural network processing and soft constraints based on the assumption of a globally continuous ground manifold. Exteroceptive sensors such as cameras,…
In this paper, we present BirdSLAM, a novel simultaneous localization and mapping (SLAM) system for the challenging scenario of autonomous driving platforms equipped with only a monocular camera. BirdSLAM tackles challenges faced by other…
We propose SpatialLLM, a novel approach advancing spatial intelligence tasks in complex urban scenes. Unlike previous methods requiring geographic analysis tools or domain expertise, SpatialLLM is a unified language model directly…
LiDAR-based SLAM is recognized as one effective method to offer localization guidance in rough environments. However, off-the-shelf LiDAR-based SLAM methods suffer from significant pose estimation drifts, particularly components relevant to…
To cover a set of targets with known locations within an area with limited or prohibited ground access using a wireless sensor network, one approach is to deploy the sensors remotely, from an aircraft. In this approach, the lack of precise…
We envision that in the near future, humanoid robots would share home space and assist us in our daily and routine activities through object manipulations. One of the fundamental technologies that need to be developed for robots is to…
Spherical robots offer unique advantages for mapping applications in hazardous or confined environments, thanks to their protective shells and omnidirectional mobility. This work presents two complementary spherical mapping systems: a…
Decentralized multi-robot LiDAR-SLAM is essential for collaborative missions but faces significant challenges in maintaining global consistency. Existing frameworks predominantly rely on local-search optimization or one-time coordinate…
In this work, we have implemented a SLAM-assisted navigation module for a real autonomous vehicle with unknown dynamics. The navigation objective is to reach a desired goal configuration along a collision-free trajectory while adhering to…
SLAM is a fundamental component of modern autonomous systems, providing robots and their operators with a deeper understanding of their environment. SLAM systems often encounter challenges due to the dynamic nature of robotic motion,…
Active Simultaneous Localization and Mapping (SLAM) is the problem of planning and controlling the motion of a robot to build the most accurate and complete model of the surrounding environment. Since the first foundational work in active…
Indoor navigation is a challenging activity for persons with disabilities, particularly, for those with low vision and visual impairment. Researchers have explored numerous solutions to resolve these challenges; however, several issues…
Simultaneous Localization and Mapping (SLAM) is considered to be a fundamental capability for intelligent mobile robots. Over the past decades, many impressed SLAM systems have been developed and achieved good performance under certain…
This work presents the Human Navigation Simulator (HuNavSim), a novel open-source tool for the simulation of different human-agent navigation behaviors in scenarios with mobile robots. The tool, the first programmed under the ROS 2…
Simultaneous Localization And Mapping (SLAM) is a fundamental problem in mobile robotics. While point-based SLAM methods provide accurate camera localization, the generated maps lack semantic information. On the other hand, state of the art…