Real-Time Free Space Semantic Segmentation for Detection of Traversable Space for an Intelligent Wheelchair

Messiou, Chrysovalanto; Fusaro, Daniel; Beraldo, Gloria; Tonin, Luca

doi:10.1109/icorr55369.2022.9896524

Cited by 2 publications

(2 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fueled by the advances in deep learning, considerable effort has been put towards developing algorithms utilizing RGB and RGB-D data for free space identification and, subsequently, path planning and obstacle avoidance ( Li and Birchfield, 2010 ; Zhao et al, 2018 ; Seichter et al, 2021 ). Deep learning-based methods can be used stand-alone to reduce costs compared to a LiDAR sensor suite ( Messiou et al, 2022 ; Dang and Bui, 2023 ), or as a supervising method to improve the perception capabilities of mobile robots ( Juel et al, 2018 ; Liu et al, 2021 ; Arapis et al, 2023 ). Despite these advancements, certain challenges persist, including segmenting free space under varying floor conditions and further reducing computational requirements for real-time applications.…”

Section: Related Workmentioning

confidence: 99%

Efficient human 3D localization and free space segmentation for human-aware mobile robots in warehouse facilities

Arapis,

Jami,

Nalpantidis

2023

Front. Robot. AI

View full text Add to dashboard Cite

Real-time prediction of human location combined with the capability to perceive obstacles is crucial for socially-aware navigation in robotics. Our work focuses on localizing humans in the world and predicting the free space around them by incorporating other static and dynamic obstacles. We propose a multi-task learning strategy to handle both tasks, achieving this goal with minimal computational demands. We use a dataset captured in a typical warehouse environment by mounting a perception module consisting of a Jetson Xavier AGX and an Intel L515 LiDAR camera on a MiR100 mobile robot. Our method, which is built upon prior works in the field of human detection and localization demonstrates improved results in difficult cases that are not tackled in other works, such as human instances at a close distance or at the limits of the field of view of the capturing sensor. We further extend this work by using a lightweight network structure and integrating a free space segmentation branch that can independently segment the floor space without any prior maps or 3D data, relying instead on the characteristics of the floor. In conclusion, our method presents a lightweight and efficient solution for predicting human 3D location and segmenting the floor space for low-energy consumption platforms, tested in an industrial environment.

show abstract

Section: Related Workmentioning

confidence: 99%

Efficient human 3D localization and free space segmentation for human-aware mobile robots in warehouse facilities

Arapis,

Jami,

Nalpantidis

2023

Front. Robot. AI

View full text Add to dashboard Cite

show abstract

“…Image segmentation aims to partition the image into meaningful regions, preserving the boundaries and properties of the objects in the scene. Free space segmentation is one of its applications, referring to the process of using a computer vision model to divide images into traversable space and occupied space (obstacles) [ 29 , 30 , 31 , 32 ]. The model’s input can be monocular or stereo images; the output is pixel-wise segmentation.…”

Section: Related Workmentioning

confidence: 99%

A Depth-Based Hybrid Approach for Safe Flight Corridor Generation in Memoryless Planning

Nguyen

Murshed

Choudhury

et al. 2023

Sensors

View full text Add to dashboard Cite

This paper presents a depth-based hybrid method to generate safe flight corridors for a memoryless local navigation planner. It is first proposed to use raw depth images as inputs in the learning-based object-detection engine with no requirement for map fusion. We then employ an object-detection network to directly predict the base of polyhedral safe corridors in a new raw depth image. Furthermore, we apply a verification procedure to eliminate any false predictions so that the resulting collision-free corridors are guaranteed. More importantly, the proposed mechanism helps produce separate safe corridors with minimal overlap that are suitable to be used as space boundaries for path planning. The average intersection of union (IoU) of corridors obtained by the proposed algorithm is less than 2%. To evaluate the effectiveness of our method, we incorporated it into a memoryless planner with a straight-line path-planning algorithm. We then tested the entire system in both synthetic and real-world obstacle-dense environments. The obtained results with very high success rates demonstrate that the proposed approach is highly capable of producing safe corridors for memoryless local planning.

show abstract

Real-Time Free Space Semantic Segmentation for Detection of Traversable Space for an Intelligent Wheelchair

Cited by 2 publications

References 20 publications

Efficient human 3D localization and free space segmentation for human-aware mobile robots in warehouse facilities

Efficient human 3D localization and free space segmentation for human-aware mobile robots in warehouse facilities

A Depth-Based Hybrid Approach for Safe Flight Corridor Generation in Memoryless Planning

Contact Info

Product

Resources

About