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- W4387389637 abstract "We present an autonomous navigation system that operates without assuming HD LiDAR maps of the environment. Our system, ALT-Pilot, relies only on publicly available road network information and a sparse (and noisy) set of crowdsourced language landmarks. With the help of onboard sensors and a language-augmented topometric map, ALT-Pilot autonomously pilots the vehicle to any destination on the road network. We achieve this by leveraging vision-language models pre-trained on web-scale data to identify potential landmarks in a scene, incorporating vision-language features into the recursive Bayesian state estimation stack to generate global (route) plans, and a reactive trajectory planner and controller operating in the vehicle frame. We implement and evaluate ALT-Pilot in simulation and on a real, full-scale autonomous vehicle and report improvements over state-of-the-art topometric navigation systems by a factor of 3x on localization accuracy and 5x on goal reachability" @default.
- W4387389637 created "2023-10-06" @default.
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- W4387389637 date "2023-10-03" @default.
- W4387389637 modified "2023-10-07" @default.
- W4387389637 title "ALT-Pilot: Autonomous navigation with Language augmented Topometric maps" @default.
- W4387389637 doi "https://doi.org/10.48550/arxiv.2310.02324" @default.
- W4387389637 hasPublicationYear "2023" @default.
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