Papers for

robotics engineering teams

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Chain-SLAM improves map alignment for multi-session LiDAR mapping

Chain-SLAM: Globally Consistent Backend for Multi-Session LiDAR SLAM via Chained Loop Closure

Abstract: Maintaining consistency over long spatial and temporal horizons remains a fundamental challenge in large-scale LiDAR SLAM, particularly when integrating maps collected across multiple sessions. We present Chain-SLAM, a LiDAR SLAM backend enabling online multi-session map alignment and reuse with global consistency at large scale. We implement a chained loop closure mechanism that efficiently propagates geometric constraints across inter-session keyframes through an adjacency graph, enabling robust long-horizon consistency triggered by reliable short-horizon loop closures. The system initializes inter-session alignment with GNSS-proximity place recognition, then performs on-the-fly loop closure detections and joint optimization of loaded maps and newly acquired trajectories within a unified factor graph, maintaining both inter- and intra-session geometric consistency without dynamic object removal, and cross-platform robustness with minimal hyperparameter tuning. Experimental results show improved trajectory accuracy and robust multi-session integration on large-scale datasets. We release our source code to support reproducible research in large-scale multi-session LiDAR SLAM. Project site: https://ai4ce.github.io/Chain-SLAM/

Thu 10 SeptRobotics
The gist
LiDAR SLAM helps create maps by tracking a sensor as it moves, but it can be hard to keep maps consistent when combining data from many sessions. The authors made Chain-SLAM, a system that better aligns and merges these maps by chaining together loop closures between sessions. This method helps correct errors over long distances and times, resulting in more accurate and stable maps. Their approach uses GPS data to start matching places and adjusts the entire map as new data arrives.
Open → 2609.12221v1

Duet-DINO improves robot grasping using two camera views

DUET-DINO: Simultaneous Cross-View World Modeling for Latent Planning in Robot Manipulation

Abstract: Action-conditioned latent world models predict future visual representations, enabling zero-shot goal-conditioned robot planning and control. However, their predictions for fine-grained spatial and rotational actions are unreliable for full 7-DoF end-effector control. To address this gap, we introduce DUET-DINO, a simultaneous cross-view latent world model that jointly learns action-conditioned predictions from static side- and wrist-camera observations through cross-view conditioning. By exploiting complementary global scene and gripper-centric information, DUET-DINO enables latent planning over the full 7-DoF action space. Across spatially diverse reach, orientation-intensive angled-reach, and multi-goal grasp-and-lift tasks, DUET-DINO consistently outperforms single-view and independent dual-view baselines, achieving 92% success on reach, 72.5% on angled-reach, and 60.0% on lift tasks. DUET-DINO is trained from scratch on DROID and RoboArena datasets and generalizes robustly under visual distribution shifts. We further show that while V-JEPA 2 wrist-view predictions underestimate visual dynamics induced by fine-grained actions, DINOv3 predictions better capture action-conditioned scene changes, leading to stronger downstream planning. The code and model checkpoints will be open-sourced. Project page: https://utn-air.github.io/DUET-DINO

Wed 9 SeptRoboticsComputer Vision and Pattern Recognition
The gist
Robots often plan their movements by predicting how the world will look after each action, but this can be tricky when the robot’s hand moves precisely in all directions. The authors introduce Duet-DINO, a system that learns to predict from two camera views at once — one from the side and one from the robot’s wrist — so it can better understand how actions will change what it sees. This helps the robot plan more accurate movements with its full range of motion, leading to improved success in reaching and grasping tasks. Their method works well even when the visual environment changes and outperforms approaches using only one camera view.
Open → 2609.10506v1