NavAgent: Multi-scale Urban Street View Fusion For UAV Embodied Vision-and-Language Navigation

• URL: http://arxiv.org/abs/2411.08579v1
• Date: Wed, 13 Nov 2024 12:51:49 GMT
• Title: NavAgent: Multi-scale Urban Street View Fusion For UAV Embodied Vision-and-Language Navigation
• Authors: Youzhi Liu, Fanglong Yao, Yuanchang Yue, Guangluan Xu, Xian Sun, Kun Fu,
• Abstract summary: Vision-and-Language Navigation (VLN) aims to enable embodied agents to navigate in complicated visual environments through natural language commands. We propose NavAgent, the first urban UAV embodied navigation model driven by a large Vision-Language Model. We build a visual recognizer for landmark capable of identifying and linguisticizing fine-grained landmarks. To train the visual recognizer for landmark, we develop NavAgent-Landmark2K, the first fine-grained landmark dataset for real urban street scenes.
• Score: 15.628308089720269
• License:
• Abstract: Vision-and-Language Navigation (VLN), as a widely discussed research direction in embodied intelligence, aims to enable embodied agents to navigate in complicated visual environments through natural language commands. Most existing VLN methods focus on indoor ground robot scenarios. However, when applied to UAV VLN in outdoor urban scenes, it faces two significant challenges. First, urban scenes contain numerous objects, which makes it challenging to match fine-grained landmarks in images with complex textual descriptions of these landmarks. Second, overall environmental information encompasses multiple modal dimensions, and the diversity of representations significantly increases the complexity of the encoding process. To address these challenges, we propose NavAgent, the first urban UAV embodied navigation model driven by a large Vision-Language Model. NavAgent undertakes navigation tasks by synthesizing multi-scale environmental information, including topological maps (global), panoramas (medium), and fine-grained landmarks (local). Specifically, we utilize GLIP to build a visual recognizer for landmark capable of identifying and linguisticizing fine-grained landmarks. Subsequently, we develop dynamically growing scene topology map that integrate environmental information and employ Graph Convolutional Networks to encode global environmental data. In addition, to train the visual recognizer for landmark, we develop NavAgent-Landmark2K, the first fine-grained landmark dataset for real urban street scenes. In experiments conducted on the Touchdown and Map2seq datasets, NavAgent outperforms strong baseline models. The code and dataset will be released to the community to facilitate the exploration and development of outdoor VLN.

Related papers

• CityNav: Language-Goal Aerial Navigation Dataset with Geographic Information [25.51740922661166]
  Vision-and-language navigation (VLN) aims to guide autonomous agents through real-world environments by integrating visual and linguistic cues. We introduce CityNav, a novel dataset explicitly designed for language-guided aerial navigation in 3D environments of real cities. CityNav comprises 32k natural language descriptions paired with human demonstration trajectories, collected via a newly developed web-based 3D simulator.
  arXiv  Detail & Related papers  (2024-06-20T12:08:27Z)
• SayNav: Grounding Large Language Models for Dynamic Planning to Navigation in New Environments [14.179677726976056]
  SayNav is a new approach that leverages human knowledge from Large Language Models (LLMs) for efficient generalization to complex navigation tasks. SayNav achieves state-of-the-art results and even outperforms an oracle based baseline with strong ground-truth assumptions by more than 8% in terms of success rate.
  arXiv  Detail & Related papers  (2023-09-08T02:24:37Z)
• AerialVLN: Vision-and-Language Navigation for UAVs [23.40363176320464]
  We propose a new task named AerialVLN, which is UAV-based and towards outdoor environments. We develop a 3D simulator rendered by near-realistic pictures of 25 city-level scenarios. We find that there is still a significant gap between the baseline model and human performance, which suggests AerialVLN is a new challenging task.
  arXiv  Detail & Related papers  (2023-08-13T09:55:04Z)
• VELMA: Verbalization Embodiment of LLM Agents for Vision and Language Navigation in Street View [81.58612867186633]
  Vision and Language Navigation(VLN) requires visual and natural language understanding as well as spatial and temporal reasoning capabilities. We show that VELMA is able to successfully follow navigation instructions in Street View with only two in-context examples. We further finetune the LLM agent on a few thousand examples and achieve 25%-30% relative improvement in task completion over the previous state-of-the-art for two datasets.
  arXiv  Detail & Related papers  (2023-07-12T11:08:24Z)
• Weakly-Supervised Multi-Granularity Map Learning for Vision-and-Language Navigation [87.52136927091712]
  We address a practical yet challenging problem of training robot agents to navigate in an environment following a path described by some language instructions. To achieve accurate and efficient navigation, it is critical to build a map that accurately represents both spatial location and the semantic information of the environment objects. We propose a multi-granularity map, which contains both object fine-grained details (e.g., color, texture) and semantic classes, to represent objects more comprehensively.
  arXiv  Detail & Related papers  (2022-10-14T04:23:27Z)
• A New Path: Scaling Vision-and-Language Navigation with Synthetic Instructions and Imitation Learning [70.14372215250535]
  Recent studies in Vision-and-Language Navigation (VLN) train RL agents to execute natural-language navigation instructions in photorealistic environments. Given the scarcity of human instruction data and limited diversity in the training environments, these agents still struggle with complex language grounding and spatial language understanding. We take 500+ indoor environments captured in densely-sampled 360 degree panoramas, construct navigation trajectories through these panoramas, and generate a visually-grounded instruction for each trajectory. The resulting dataset of 4.2M instruction-trajectory pairs is two orders of magnitude larger than existing human-annotated datasets.
  arXiv  Detail & Related papers  (2022-10-06T17:59:08Z)
• Think Global, Act Local: Dual-scale Graph Transformer for Vision-and-Language Navigation [87.03299519917019]
  We propose a dual-scale graph transformer (DUET) for joint long-term action planning and fine-grained cross-modal understanding. We build a topological map on-the-fly to enable efficient exploration in global action space. The proposed approach, DUET, significantly outperforms state-of-the-art methods on goal-oriented vision-and-language navigation benchmarks.
  arXiv  Detail & Related papers  (2022-02-23T19:06:53Z)
• SOON: Scenario Oriented Object Navigation with Graph-based Exploration [102.74649829684617]
  The ability to navigate like a human towards a language-guided target from anywhere in a 3D embodied environment is one of the 'holy grail' goals of intelligent robots. Most visual navigation benchmarks focus on navigating toward a target from a fixed starting point, guided by an elaborate set of instructions that depicts step-by-step. This approach deviates from real-world problems in which human-only describes what the object and its surrounding look like and asks the robot to start navigation from anywhere.
  arXiv  Detail & Related papers  (2021-03-31T15:01:04Z)
• Structured Scene Memory for Vision-Language Navigation [155.63025602722712]
  We propose a crucial architecture for vision-language navigation (VLN) It is compartmentalized enough to accurately memorize the percepts during navigation. It also serves as a structured scene representation, which captures and disentangles visual and geometric cues in the environment.
  arXiv  Detail & Related papers  (2021-03-05T03:41:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.