3 Key Questions: Enhancing Robot Perception and Mapping for Optimal Performance | MIT News
Introduction:
The MIT Laboratory for Information and Decision Systems (LIDS) researchers Luca Carlone and Jonathan How are revolutionizing the way robots perceive and interact with their environment. They have developed the open-source library called Kimera, which allows robots to construct real-time three-dimensional maps of their surroundings and identify different objects. Recently, their research groups released Kimera-Multi, an updated system that enables multiple robots to communicate with each other and create a unified map. The system has garnered recognition, with their associated paper receiving the prestigious IEEE Transactions on Robotics King-Sun Fu Memorial Best Paper Award. Carlone and How envision a future where robots can collaborate to enhance safety, improve mapping efforts, and assist in search and rescue missions. With their advancements, the possibilities for enhancing human-robot cooperation are endless.
Full Article: 3 Key Questions: Enhancing Robot Perception and Mapping for Optimal Performance | MIT News
Robots that can perceive and interact with their environment in a human-like way are a topic of interest for researchers at MIT Laboratory for Information and Decision Systems (LIDS). Luca Carlone and Jonathan How, along with their respective research groups, have been working on developing technologies that enable robots to construct three-dimensional maps of their surroundings in real time. Their latest system, called Kimera-Multi, allows multiple robots to communicate and create a unified map. Recognizing the significance of their work, a recent paper associated with the project received the prestigious IEEE Transactions on Robotics King-Sun Fu Memorial Best Paper Award for 2022.
Advantages of Scaling the System
One of the main advantages of scaling the system is achieving consistency in mapping. Each robot can create an independent map that is self-consistent, but the goal is to form a consensus between robots to have a globally consistent map. This consistency is crucial when it comes to tasks like search-and-rescue missions, where having multiple robots exploring increases the chances of success. Additionally, scaling up the team of robots can lead to faster completion of tasks.
Challenges and Lessons Learned
Recent experiments conducted by Carlone and his team involved eight robots mapping the MIT campus, covering a total distance of up to 8 kilometers. The robots had no prior knowledge of the campus or GPS. The challenge was for the robots to understand the environment in a way that resembles how humans understand it. This includes not only perceiving the shape of obstacles to avoid collisions, but also recognizing and labeling objects with semantic meaning. To improve the map, the robots exchange information when they encounter each other, correcting their own trajectories. However, the limited bandwidth for data exchange requires the development of a distributed protocol that allows robots to reach a consensus on the map using only specific 3D coordinates and clues extracted from sensor data.
Moving Towards a More Hierarchical Representation
While the current system can generate color-coded 3D maps with some semantic information, the researchers aim to move towards a more hierarchical representation of the environment. Humans have a sophisticated understanding of reality and prior knowledge about relationships between objects. By incorporating this complex understanding, the robots can make smarter decisions in the environment. The goal is to expand the system’s understanding from capturing one layer of semantics to understanding rooms, buildings, and other concepts.
Future Applications of Kimera and Similar Technologies
The implications of Kimera and similar technologies extend beyond research labs. Autonomous vehicle companies, for example, could greatly benefit from improved mapping and model sharing between vehicles. The ability for vehicles to communicate and share information would enhance safety by providing data from multiple perspectives. Additionally, these technologies have potential applications in areas such as search and rescue missions, mapping after natural disasters, and flexible factory environments where robots need to interact with humans in less structured spaces.
The work of Carlone, How, and their teams at MIT LIDS represents a significant step towards enabling robots to perceive and interact with their environment in a manner similar to humans. The advancements in mapping and consensus building among robots hold great promise for various industries and scenarios where sophisticated understanding and interaction with the environment are crucial.
Summary: 3 Key Questions: Enhancing Robot Perception and Mapping for Optimal Performance | MIT News
Researchers from MIT Laboratory for Information and Decision Systems (LIDS) have developed an open-source library called Kimera that allows robots to construct a real-time, three-dimensional map of their environment and label objects. The team also introduced Kimera-Multi, a system where multiple robots communicate to create a unified map. Their work recently won the IEEE Transactions on Robotics King-Sun Fu Memorial Best Paper Award. The researchers aim to increase the number of robots that can work together to generate 3D maps, leading to advantages such as consistency, redundancy, and shorter completion times. Potential applications include search and rescue, mapping after disasters, and flexible factories.
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