Natural environments present a complex challenge to robotics perception systems. Current models, particularly vision foundation models, are largely trained on structured, urban environments leading to weaknesses in their perception for field robotics tasks. We showcase the limitations of current models using our recently released WildCross benchmark, a new cross-modal benchmark for place recognition and metric depth estimation in large-scale natural environments. WildCross comprises over 476K sequential RGB frames with semi-dense depth and surface normal annotations, each aligned with accurate 6DoF pose and synchronized dense lidar submaps. In this work, we provide an expanded analysis of the benchmark results from the recent WildCross benchmark, with particular emphasis on expanded metric depth estimation experiments. Access to the code repository and dataset for this work can be found at https://csiro-robotics.github.io/WildCross.

翻译：自然环境为机器人感知系统带来了复杂的挑战。当前模型，尤其是视觉基础模型，大多在结构化的城市环境中训练，导致其在野外机器人任务中的感知能力存在薄弱环节。我们利用近期发布的WildCross基准测试——一个面向大规模自然环境下的位置识别与度量深度估计的新型跨模态基准——展示了当前模型的局限性。WildCross包含超过47.6万帧序列RGB图像及其半稠密深度和表面法线标注，每一帧均与精确的六自由度位姿及同步的稠密激光雷达子图对齐。本研究对近期WildCross基准测试的结果进行了扩展分析，特别侧重于扩展的度量深度估计实验。本工作的代码仓库与数据集访问地址为：https://csiro-robotics.github.io/WildCross。