This project develops MISO, a hierarchical optimization approach that leverages multiresolution submaps to achieve efficient and scalable neural implicit reconstruction. For local SLAM within each submap, we develop a hierarchical optimization scheme with learned initialization that substantially reduces the time needed to optimize the implicit submap features. To correct estimation drift globally, we develop a hierarchical method to align and fuse the multiresolution submaps, leading to substantial acceleration by avoiding the need to decode the full scene geometry. MISO significantly improves computational efficiency and estimation accuracy of neural signed distance function (SDF) SLAM on large-scale real-world benchmarks.

This project develops the first approach for distributed multi-robot dense metric-semantic mapping. In (Tian et al., T-RO'22), we present Kimera-Multi, a multi-robot system that (i) is robust to spurious loop closures resulting from incorrect place recognition, (ii) is fully distributed and only relies on local communication, and (iii) builds a globally consistent metric-semantic 3D mesh model of the environment in real-time, where faces of the mesh are annotated with semantic labels. Kimera-Multi has been demonstrated in real-world field experiments with up to 8 robots traversing up to 8km (Tian et al., IROS'23). 

IEEE Transactions on Robotics King-Sun Fu Memorial Best Paper Award, 2022.
Honorable Mention for MIT Open Data Prize, 2023.

This project develops a suite of distributed geometric optimization algorithms, the backbone of modern collaborative simultaneous localization and mapping (CSLAM) and camera network localization (CNL) systems. In (Tian et al., T-RO'21), we developed first certifiably correct algorithm for distributed pose graph optimization (PGO). In (Tian et al. RA-L'20), we extend the approach to handle delayed communication while maintaining provable convergence. In (Tian et al. T-RO'23), we further leverage spectral graph theoretic tools to achieve a significant speed up in the algorithm convergence.

Honorable Mention for IEEE Robotics and Automation Letter Best Paper Award, 2020.
Honorable Mention for IEEE Transactions on Robotics King-Sun Fu Memorial Best Paper Award, 2021.