A frozen MLLM with LoRA-on-language and a discrete token waypoint interface navigates four unseen real-world environments after just 8.7 hours of training data.
Wildcat: Online Continuous-Time 3D Lidar-Inertial SLAM
2 Pith papers cite this work. Polarity classification is still indexing.
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abstract
We present Wildcat, a novel online 3D lidar-inertial SLAM system with exceptional versatility and robustness. At its core, Wildcat combines a robust real-time lidar-inertial odometry module, utilising a continuous-time trajectory representation, with an efficient pose-graph optimisation module that seamlessly supports both the single- and multi-agent settings. The robustness of Wildcat was recently demonstrated in the DARPA Subterranean Challenge where it outperformed other SLAM systems across various types of sensing-degraded and perceptually challenging environments. In this paper, we extensively evaluate Wildcat in a diverse set of new and publicly available real-world datasets and showcase its superior robustness and versatility over two existing state-of-the-art lidar-inertial SLAM systems.
years
2026 2representative citing papers
Presents the WildCross benchmark with 476K frames for place recognition and metric depth estimation in natural environments, demonstrating limitations of existing vision models.
citing papers explorer
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GemNav: Discrete-Token Visual Robot Navigation using a Multimodal Large Language Model
A frozen MLLM with LoRA-on-language and a discrete token waypoint interface navigates four unseen real-world environments after just 8.7 hours of training data.
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Cross-Modal Benchmarking for Robotic Perception in Natural Environments
Presents the WildCross benchmark with 476K frames for place recognition and metric depth estimation in natural environments, demonstrating limitations of existing vision models.