Harnessing the power of AI and Machine Learning in lidar technology


Artificial Intelligence (AI) and Machine Learning (ML) are reshaping various industries, including the lidar software industry. Here at Flasheye we are working on our own approach to contribute to this change. Harnessing these advanced technologies to augment data analysis, object detection and tracking, and automated feature extraction in real-time lidar monitoring solutions. However, while AI and ML offer substantial benefits, they are not always the most suitable tools for every application. In safety and security-critical applications, traditional rule-based analysis can often provide more reliable and efficient solutions. In this article we would like to touch on how we think about this.

One of the challenges we face is processing and analysing the massive amount of lidar data we collect and deliver to our customers. Here at Flasheye, we are working on two main approaches for this purpose: rule-based analysis and artificial intelligence/machine learning (AI/ML).
Rule-based analysis is a method that relies on predefined rules and criteria to extract information from the data. For example, a rule-based system can identify objects in the lidar data by comparing their shapes and sizes to a database of known objects. Rule-based analysis has some advantages, such as being fast, consistent, easy to understand and debug. However, it also has some limitations, such as being rigid, inflexible, and unable to handle complex or novel situations.

AI/ML uses algorithms and models that can learn from the data and improve their performance over time. For example, an AI/ML system can identify objects in lidar data by using neural networks that can recognise patterns and features in the data. AI/ML has some advantages, such as being adaptable, flexible, and able to handle complex or novel situations that rule-based analysis struggles with. However, it also has its own challenges, such as being computationally intensive, requiring large amounts of data and resources, and being difficult to interpret and explain.

Still, the potential gains from delving into AI/ML are manifold impossible to ignore. Here are just a few of the things it will enable Flasheye to do:
- Enhance the quality and accuracy of its lidar products by reducing errors and noise in the data.
- Provide more value-added services to its customers by offering customized solutions and insights based on their specific needs and preferences.
- Gain a competitive edge in the market by leveraging the latest AI/ML research and innovation advances.
- Reduce costs and increase efficiency by automating and optimizing its data processing and analysis workflows.

For example, AI and ML can significantly enhance the processing and analysis of lidar point clouds. For instance, ML algorithms can be used to classify the 3D point cloud into elementary classes, differentiating between vegetation, man-made structures, and water. This classification is crucial in many safety and security applications, where distinguishing between different types of objects is essential for threat detection and response.

Moreover, AI and ML can learn from vast amounts of lidar data, improving the accuracy and efficiency of various applications. There are also strategies to simplify the data, like Principal Component Analysis (PCA) and methods based on neural networks that can help improve how well we can categorize or classify data in lidar point clouds.

This adaptability synergises very well with Flasheye’s company ethos and our development process. Our real-time lidar monitoring solutions are designed to detect changes and dangers without compromising privacy and integrity. By integrating AI and ML into our workflow, we can further enhance the precision and versatility of our safety and security solutions.

AI and ML can also help Flasheye's software solutions to detect deviations, spillage, and foreign objects at an early stage, preventing serious damage. They can also improve the accuracy of measurements on truck beds, stockpiles, and conveyor belts, enabling better production control based on 3D data.

Moreover, AI and ML can enhance our future public safety applications, helping to detect and react to dangerous situations in tunnels, traffic, and crowded areas, improving public safety while respecting individual privacy in regards to GDPR.

While AI and ML offer significant benefits, they are not always the most suitable tools for every application. In safety and security-critical applications, for example, it is crucial that all scenarios are fully understood. AI and ML algorithms learn from data and can make predictions or decisions based on patterns they detect. However, if they encounter a scenario that is not represented in the data they were trained on, they may not respond appropriately. In contrast, rule-based analysis follows predefined rules and can provide more predictable and reliable results in such cases.
Furthermore, AI and ML require specialized hardware for better processing capacity, which can increase the complexity and cost of the system. Rule-based analysis, on the other hand, can often be performed on standard hardware, making it a more cost-effective solution in some cases.

However, it's important to note that AI and ML can handle complex tasks that would be difficult, if not impossible, to perform with rule-based analysis. They can learn from vast amounts of data and improve their performance over time, making them highly effective for tasks such as object detection and tracking, and automated feature extraction.

In order to rightfully balance these two approaches we must be aware of the pros and cons of using AI/ML for our lidar products as a service, if we are to successfully adopt a balanced and hybrid approach that combines the strengths of both rule-based analysis and AI/ML. Here are some of the most important things we can do to accomplish this:
- Ensure the reliability and robustness of our AI/ML systems by testing and validating them regularly.
- Ensure the security and privacy of our data and customers by following ethical and legal standards and regulations.
- Ensure the transparency and accountability of our AI/ML systems by providing clear explanations and justifications for its decisions and actions.
- Ensure the collaboration and communication between our human experts and AI/ML systems by establishing trust and feedback mechanisms.

The integration of AI and ML into lidar software has the potential to revolutionize the industry, and Flasheye will be right there with it. However, while these technologies offer significant benefits, they are not always the most suitable tools for every application. By balancing the use of AI and ML with traditional rule-based analysis, Flasheye can ensure that they provide the most effective and reliable solutions for each application. As AI and ML continue to evolve, we evolve with it. So expect to see even more innovative solutions from Flasheye in the future, always with a keen eye on the balance between innovation and reliability.