Object Detection via Point Cloud Data
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".
Deadline for manuscript submissions: 15 December 2024 | Viewed by 500
Special Issue Editors
Interests: mathematical modeling; model predictive control; estimation; multi-body dynamics; mechatronics; field robotics; mining automation; technology implementation
Special Issues, Collections and Topics in MDPI journals
Interests: robot
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Autonomous platforms often require the ability to interact with objects in their immediate environment. These interactions can manifest, for example, as simple pick-and-place operations or as complex coordinations between multiple robotic agents. Nonetheless, at either end of the complexity scale, the decision-making requires to perform interactions requires an a priori level of situational awareness.
Situational awareness encapsulates the ability to perceive, understand and predict elements within a given environment to make informed decisions. A key belief required to facilitate object interactions is to first detect the object. Detection, in this sense, amounts to determining (i) where an object is and (ii) what an object is. This detection takes place at the perception and comprehension stages of situational awareness and is often facilitated via the interpretation of dense 3D point cloud measurements.
Determining where an object is extends to detecting if an object is indeed present within the point cloud data or identifying when multiple objects are present. Likewise, the determination of what an object is can involve selecting the most likely candidate from a catalogue of known object types or verifying that the assumed geometry is correct to some degree of tolerance.
The final stage of providing situational awareness is the ability to predict future states. Pertaining to object detection, this might include where an object is likely to move, informed by an understanding of its intent or the constraints of its environment. It might also include whether an object is at risk or presents a threat to something else, e.g., collision avoidance.
This Special Issue requests papers that address problems related to object detection via point cloud data, as well as the limitations of existing approaches.
Prof. Dr. Peter Ross McAree
Dr. Tyson Phillips
Guest Editors
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Keywords
- object detection
- perception
- situational awareness
- pose estimation
- object classification
- point cloud
- LiDAR
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