What are the current trends and future prospects for LiDAR and Robotics professionals?

The future of lidar is going to include non-mechanical scanners (meta-surfaces one example) to implement fully digital beam steering and fmcw lidars which detect both range and velocity. Another direction in which lidars will improve is the processing stage will include more ML based compute for point cloud generation.

In the world of AI & LiDAR join, let's explore a fusion of tech, ethics, & innovation. Together, LiDAR & Robotics professionals chart course towards endless possibilities & collaborative success in Industry 4.0 era. LiDAR tech keeps getting better for different robot needs. The focus is on making smaller, cheaper LiDAR sensors that work super well in all sorts of robots. They're also working on making the data from LiDAR even more detailed and reliable for 3D mapping. Smart tech like AI & machine learning is helping process LiDAR info better. They're teaming up with LiDAR with other sensors like cameras and radars to give robots even more useful info. All in all, it's about making robots smarter & more aware of their surroundings.

Application Expansion: LiDAR technology is finding applications beyond traditional uses in mapping and surveying. It is increasingly utilized in agriculture, forestry, infrastructure monitoring, environmental conservation, and other fields where accurate 3D spatial data is essential. AI and Machine Learning Integration: The integration of artificial intelligence (AI) and machine learning (ML) techniques is helping LiDAR systems better interpret and analyze the collected data. This trend contributes to improved object recognition, classification, and scene understanding.

Variety: There is no fit-all technology as more and more application has been explored. Each combination of ranging principles, scanning patterns, wavelength and more, seems reasonable for different requests at different scenario. High resolution: Companies are looking forward to photo-like(720P) LiDAR data, then they can directly adopt the camera-based algorithms stack. Sensor fusion: Integrating other sensors (cameras and IMUs) into a LiDAR can cut off many hardware as well as calibration/sync work for users.

LiDAR technology, especially when integrated with Artificial Intelligence (AI) and deep learning, holds significant promise for robotics applications. The increasing adoption of edge AI for on-device processing of LiDAR data further contributes to this potential. Semantic Segmentation: Utilizing deep learning techniques for the semantic segmentation of LiDAR point clouds allows robots to distinguish between different objects and understand the context. Predictive Analytics: AI models integrated with LiDAR could enable robots to predict and anticipate future events or obstacles, enhancing their ability to plan and navigate efficiently.

Functionalities of LiDAR system on robotics： Safety: Collision avoidance & blind spots detection; Passable area detection Navigate: LiDAR can navigate a platform combining with lane-keeping, auto-steering, as well as SLAM technology Motion control：LiDAR point cloud enables machines to execute tasks with high precision requirements, like pick, excavate, drill, spray, etc. Topology：LiDAR can help to capture the topology of windrow, grains, mineral, etc, for volume measurement and occupancy level detection

In the rapidly evolving landscape of LiDAR and robotics, certain skills and qualifications have proven crucial: Programming Proficiency: Proficiency in programming languages such as Python and C++ is essential. Understanding the software frameworks used in LiDAR and robotics applications, such as ROS (Robot Operating System), is highly advantageous. Sensor Fusion Expertise: Skills in sensor fusion, combining data from LiDAR with other sensors like cameras and IMUs, enhance the capabilities of robotic systems. Machine Learning: As machine learning plays an increasing role in perception and decision-making for robots, familiarity with ML algorithms and frameworks becomes a valuable asset.

Tools like Autodesk ReCap, Trimble RealWorks, PointCab, offer a wide range of features, from data capture and processing to visualization and analysis, enabling professionals to extract valuable insights and make informed decisions. A vender usually provides user's with customized point cloud view software, ROS driver, SDK/API.

LiDAR itself is a multidisciplinary products combining different expertise: mechanism, electricity, optics, FPGA, control, etc. LiDAR is a critical sensor for AI.

Lidar technology is developing rapidly, as illustrated by the number of start-up companies to serve the autonomous market which feature traditional mechanical scanning devices, tiny-chip solid-state Lidar units and the emerging FMCW Lidar. The fact that Apple has announced an iPad with Lidar indicates this technology’s arrival in the public domain.

LiDAR & Robotics professionals should keep an eye on trends like AI integration, miniaturization, & cost reduction of LiDAR sensors. Collaboration, ethical considerations, & upskilling are crucial. Regulatory factors, data security, & sensor reliability need attention. Hardware advancements, interdisciplinary work, & industry-specific applications are shaping field. Startups, open-source projects, & public perception play key roles. Startups & innovation hubs drive industry-specific developments, while open-source projects contribute to knowledge transfer. Public perception, ethical AI, & intellectual property considerations are vital for the widespread acceptance & success of LiDAR & Robotics in the evolving Industry 4.0 landscape.