Target Position and Speed Estimation Using LiDAR

Dayangac, Enes; Baumann, Florian; Aulinas, Josep; Zobel, Matthias

doi:10.1007/978-3-319-41501-7_53

Cited by 3 publications

(3 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The robot detects and identifies an object utilizing on-board sensors. The robot identifies a nearby object utilizing its camera [21][22][23][24] and Lidar [25,26,52,53]. How to identify objects using on-board sensors is not within the scope of our paper.…”

Section: Robot Motion and Obstacle Avoidancementioning

confidence: 99%

“…The robot can measure an objectBoundaryPoint O ∈ R 2 and its velocity using its on-board sensors, such as Lidar [52,53]. Since the robot can locate itself, it can access q(k) ∈ R 2 and v(k) ∈ R 2 in (2) at each sample-step k. Therefore, the robot can access the position and velocity of an objectBoundaryPoint in real time.…”

Section: Robot Motion and Obstacle Avoidancementioning

confidence: 99%

See 1 more Smart Citation

Path-Following and Collision-Avoidance Controls of a Robot in a Large Building with One or More Elevators

Kim

2023

Applied Sciences

View full text Add to dashboard Cite

For planning a robot’s path inside a large building with one or more elevators, we develop a topological map, called the building Voronoi graph. Using the building Voronoi graph, the robot finds the shortest path to the goal and follows the path. In the case where the robot detects an object with arbitrary shapes (e.g., human) while following the path, the robot avoids the object utilizing reactive control laws. The proposed reactive collision-avoidance control is unique in considering collision avoidance with map environments as well as (moving or static) objects having arbitrary shapes. As far as we know, our paper is novel in addressing how to make the robot follow the building Voronoi graph, while avoiding collision with map environments as well as objects with arbitrary shapes.

show abstract

Section: Robot Motion and Obstacle Avoidancementioning

confidence: 99%

Section: Robot Motion and Obstacle Avoidancementioning

confidence: 99%

Path-Following and Collision-Avoidance Controls of a Robot in a Large Building with One or More Elevators

Kim

2023

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…The existing methods map the area using LiDAR or stereo vision camera to get estimated positions of any moving objects like car [40], robots, or drones in an unknown environment. This sensor module can measure the distance from a drone to other objects, like walls, trees, vehicles, etc., in real-time.…”

Section: Lidar-vision Sensor Comparison For Localization and Area Mappingmentioning

confidence: 99%

Multiple Drone Navigation and Formation Using Selective Target Tracking-Based Computer Vision

2021

View full text Add to dashboard Cite

Autonomous unmanned aerial vehicles work seamlessly within the GPS signal range, but their performance deteriorates in GPS-denied regions. This paper presents a unique collaborative computer vision-based approach for target tracking as per the image’s specific location of interest. The proposed method tracks any object without considering its properties like shape, color, size, or pattern. It is required to keep the target visible and line of sight during the tracking. The method gives freedom of selection to a user to track any target from the image and form a formation around it. We calculate the parameters like distance and angle from the image center to the object for the individual drones. Among all the drones, the one with a significant GPS signal strength or nearer to the target is chosen as the master drone to calculate the relative angle and distance between an object and other drones considering approximate Geo-location. Compared to actual measurements, the results of tests done on a quadrotor UAV frame achieve 99% location accuracy in a robust environment inside the exact GPS longitude and latitude block as GPS-only navigation methods. The individual drones communicate to the ground station through a telemetry link. The master drone calculates the parameters using data collected at ground stations. Various formation flying methods help escort other drones to meet the desired objective with a single high-resolution first-person view (FPV) camera. The proposed method is tested for Airborne Object Target Tracking (AOT) aerial vehicle model and achieves higher tracking accuracy.

show abstract