In the Robot Operating System (ROS), a major middleware for robots, the Transform Library (TF) is a mandatory package that manages transformation information between coordinate systems by using a directed forest data structure and providing methods for registering and computing the information. However, the structure has two fundamental problems. The first is its poor scalability: since it accepts only a single thread at a time due to using a single giant lock for mutual exclusion, the access to the tree is sequential. Second, there is a lack of data freshness: it retrieves non-latest synthetic data when computing coordinate transformations because it prioritizes temporal consistency over data freshness. In this paper, we propose methods based on transactional techniques. This will allow us to avoid anomalies, achieve high performance, and obtain fresh data. These transactional methods show a throughput of up to 429 times higher than the conventional method on a read-only workload and a freshness of up to 1276 times higher than the conventional one on a read-write combined workload.