This article presents a comparative analysis of g2o and Ceres solvers in enhancing scan matching performance within the Cartographer framework. Cartographer, a widely-used library for Simultaneous Localization and Mapping (SLAM), relies on optimization algorithms to refine pose estimates and improve map accuracy. The research aims to evaluate the performance, efficiency, and accuracy of the g2o solver in comparison to the Ceres solver, which is the default in Cartographer. In our experiments comparing Ceres and g2o within Cartographer, Ceres outperformed g2o in terms of speed, convergence efficiency, and overall map clarity. Ceres required fewer iterations and less time to converge, producing more accurate and well-defined maps, especially in real-world mapping scenarios with the AgileX LIMO robot. However, g2o excelled in localized obstacle detection, highlighting its value in specific situations.

Bundle adjustment (BA) is a critical technique in various robotic applications, such as simultaneous localization and mapping (SLAM), augmented reality (AR), and photogrammetry. BA optimizes parameters such as camera poses and 3D landmarks to align them with observations. With the growing importance of deep learning in perception systems, there is an increasing need to integrate BA with deep learning frameworks for enhanced reliability and performance. However, widely-used C++-based BA frameworks, such as GTSAM, g$^2$o, and Ceres, lack native integration with modern deep learning libraries like PyTorch. This limitation affects their flexibility, adaptability, ease of debugging, and overall implementation efficiency. To address this gap, we introduce an eager-mode BA framework seamlessly integrated with PyPose, providing PyTorch-compatible interfaces with high efficiency. Our approach includes GPU-accelerated, differentiable, and sparse operations designed for 2nd-order optimization, Lie group and Lie algebra operations, and linear solvers. Our eager-mode BA on GPU demonstrates substantial runtime efficiency, achieving an average speedup of 18.5$\times$, 22$\times$, and 23$\times$ compared to GTSAM, g$^2$o, and Ceres, respectively.

Scientists may need to think twice when using artificial intelligence to help in the search for extraterrestrial life, a new study suggests. A Spanish team used an AI system that interpreted the shape of a triangle outside a square from a NASA image of a crater on the dwarf planet Ceres. Researchers then brought together 163 volunteers with no training in astronomy to describe what the saw on the image of the crater. While the AI detected a both a square and a triangle, the majority of humans only interpreted a square. But once the triangle was pointed out to the humans, the amount of people who said they could see it rose from 7 per cent to 56 per cent. This suggests the influence of AI could be strong on the human brain when interpreting alien characteristics on other planets, even when it's questionable they even exist.

Study reveals possible problems with current SETI approach. Role of artificial intelligence in current and future SETI is discussed. Computer vision model was tested vs humans in reconnaissance planetary imaging test. Today, we live in the midst of a surge in the use of artificial intelligence in many scientific and technological applications, including the Search for Extraterrestrial Intelligence (SETI). However, human perception and decision-making is still the last part of the chain in any data analysis or interpretation of results or outcomes.

Ceres may have once had a global ocean - and part of it could still remain, NASA has revealed. The dwarf planet, best known for its strange'alien spots', is seen as being a record of the early solar system. Now, the Dawn mission has found minerals containing water are widespread on its surface. Ceres is 590 miles (950 km) across and was discovered in 1801. It is the closest dwarf planet to the sun and is located in the asteroid belt between Mars and Jupiter, making it the only dwarf planet in the inner solar system.

SAN FRANCISCO – The dwarf planet Ceres, an enigmatic rocky body inhabiting the main asteroid belt between Mars and Jupiter, is rich with ice just beneath its dark surface, scientists said on Thursday in research that may shed light on the early history of the solar system. The discovery, reported in a pair of studies published in the journals Science and Nature Astronomy, could bolster fledgling commercial endeavors to mine asteroids for water and other resources for robotic and eventual human expeditions beyond the moon. NASA's Dawn spacecraft has been orbiting Ceres, the largest of thousands of rocky bodies located in the main asteroid belt, since March 2015 following 14-month study of Vesta, the second-largest object in the asteroid belt. The studies show that Ceres is about 10 percent water, now frozen into ice, according to physicist Thomas Prettyman of the Planetary Science Institute in Tucson, Arizona, one of the researchers. Examining the makeup of solar system objects like Ceres provides insight into how the solar system formed.