Autonomous Perception in Unstructured Environments

Unstructured environments present several challenges to autonomous agents such as robots and autonomous vehicles. Off-road navigation demands traversal over complex and often changing terrain, understanding which can improve path planning strategies by reducing travel time and energy consumption. A terrain classification and assessment framework has been introduced that relies on both exteroceptive and proprioceptive sensor modalities. Images of the terrain are used to train a support vector machine in an offline training phase and classify the terrain in the operating phase. Acceleration data is used to calculate statistical features that capture the roughness of the terrain and angular velocities are used to calculate roll and pitch angles. These features are used to train a k-means clustering classifier, where k is the number of anticipated terrain types. In the operating phase, cluster centers predict the vibration features associated with the terrain type. Vibration features are measured and the clusters are updated upon traversal, thus adapting to changes in terrain over time. For autonomous vehicles to viably replace human drivers, they must be able to operate in all weather conditions. There is, however, a distinct lack of datasets focused on inclement weather leading to a gap in the development of autonomous systems in such conditions. Falling rain and snow introduce noise in LiDAR returns resulting in both false positive and false negative object detections. We introduce the Winter Adverse Driving dataSet (WADS), a novel dataset collected in the snow belt region of Michigan\u27s Upper Peninsula. WADS is the first multi-modal dataset featuring dense point-wise labeled sequential LiDAR scans collected in severe winter weather; weather that would cause an experienced driver to alter their driving behavior. Our dataset features exclusively events with heavy wet snow and occasional white-out conditions. Over 26 TB of adverse winter data have been collected over three years of which over 7 GB of LiDAR points have been labeled. I also present the Dynamic Statistical Outlier Removal (DSOR) filter, a statistical PCL-based filter capable of removing snow with a higher recall than the state-of-the-art snow de-noising filter while being 28% faster. The DSOR filter is shown to have a lower time complexity, resulting in improved scalability

Winter adverse driving dataset for autonomy in inclement winter weather

The availability of public datasets with annotated light detection and ranging (LiDAR) point clouds has advanced autonomous driving tasks, such as semantic and panoptic segmentation. However, there is a lack of datasets focused on inclement weather. Snow and rain degrade visibility and introduce noise in LiDAR point clouds. In this article, summarize a 3-year winter weather data collection effort and introduce the winter adverse driving dataset. It is the first multimodal dataset featuring moderate to severe winter weather - weather that would cause an experienced driver to alter their driving behavior. Our dataset features exclusively events with heavy snowfall and occasional white-out conditions. Data are collected using high-resolution LiDAR, visible as well as near infrared (IR) cameras, a long wave IR camera, forward-facing radio detection and ranging, and Global Navigation Satellite Systems/Inertial Measurement Unit units. Our dataset is unique in the range of sensors and the severity of the conditions observed. It is also one of the only data sets to focus on rural and semi-rural environments. Over 36 TB of adverse winter data have been collected over 3 years. We also provide dense point-wise labels to sequential LiDAR scans collected in severe winter weather. We have labeled and will make available around 1000 sequential LiDAR scenes, amounting to over 7 GB or 3.6 billion labeled points. This is the first point-wise semantically labeled dataset to include falling snow

The Winter Adverse Driving dataSet (WADS) - sequence 14

Collected in the snow belt region of Michigan\u27s Upper Peninsula, WADS is the first multi-modal dataset featuring dense point-wise labeled sequential LiDAR scans collected in severe winter weather. Over 26 TB of multi modal data has been collected of which over 7 GB of LiDAR point clouds (3.6 billion points) have been labeled (semanticKITTI format) and made available here. This outdoor dataset introduces falling snow and accumulated snow along with all the semanticKITTI classes. We believe this dataset will further AV tasks like semantic and panoptic segmentation, object detection and tracking, and localization and mapping in conditions of moderate to severe snow

The Winter Adverse Driving dataSet (WADS) - sequence 26

Collected in the snow belt region of Michigan\u27s Upper Peninsula, WADS is the first multi-modal dataset featuring dense point-wise labeled sequential LiDAR scans collected in severe winter weather. Over 26 TB of multi modal data has been collected of which over 7 GB of LiDAR point clouds (3.6 billion points) have been labeled (semanticKITTI format) and made available here. This outdoor dataset introduces falling snow and accumulated snow along with all the semanticKITTI classes. We believe this dataset will further AV tasks like semantic and panoptic segmentation, object detection and tracking, and localization and mapping in conditions of moderate to severe snow

Winter Adverse Driving dataSet (WADS): Year Three

Michigan Tech\u27s unique climatology allows for relatively effortless collection of autonomous vehicle winter driving data featuring notionally severe winter weather. Over the past two years we have collected over twenty-five terabytes of winter driving data in suburban and rural settings. Year one focused on phenomenology of snowfall in the context of autonomous vehicle sensors, specifically LiDAR. Year two focused on more severe conditions, longer wavelength LiDAR, and first attempts at applying perception pipeline processing to the dataset. For year three we focus on simultaneous RADAR and LiDAR data collection in arctic-like conditions and LiDAR designs likely to be used in ADAS and production autonomous vehicles. We also introduce a point-wise labeled portion of our dataset to aid machine learning based autonomy and a snow removal filter to reduce clutter noise and improve existing object detection algorithms

The Winter Adverse Driving dataSet (WADS) - sequence 17

Collected in the snow belt region of Michigan\u27s Upper Peninsula, WADS is the first multi-modal dataset featuring dense point-wise labeled sequential LiDAR scans collected in severe winter weather. Over 26 TB of multi modal data has been collected of which over 7 GB of LiDAR point clouds (3.6 billion points) have been labeled (semanticKITTI format) and made available here. This outdoor dataset introduces falling snow and accumulated snow along with all the semanticKITTI classes. We believe this dataset will further AV tasks like semantic and panoptic segmentation, object detection and tracking, and localization and mapping in conditions of moderate to severe snow

The Winter Adverse Driving dataSet (WADS) - sequence 20

Collected in the snow belt region of Michigan\u27s Upper Peninsula, WADS is the first multi-modal dataset featuring dense point-wise labeled sequential LiDAR scans collected in severe winter weather. Over 26 TB of multi modal data has been collected of which over 7 GB of LiDAR point clouds (3.6 billion points) have been labeled (semanticKITTI format) and made available here. This outdoor dataset introduces falling snow and accumulated snow along with all the semanticKITTI classes. We believe this dataset will further AV tasks like semantic and panoptic segmentation, object detection and tracking, and localization and mapping in conditions of moderate to severe snow

The Winter Adverse Driving dataSet (WADS) - sequence 13

Collected in the snow belt region of Michigan\u27s Upper Peninsula, WADS is the first multi-modal dataset featuring dense point-wise labeled sequential LiDAR scans collected in severe winter weather. Over 26 TB of multi modal data has been collected of which over 7 GB of LiDAR point clouds (3.6 billion points) have been labeled (semanticKITTI format) and made available here. This outdoor dataset introduces falling snow and accumulated snow along with all the semanticKITTI classes. We believe this dataset will further AV tasks like semantic and panoptic segmentation, object detection and tracking, and localization and mapping in conditions of moderate to severe snow

The Winter Adverse Driving dataSet (WADS) - sequence 12

Collected in the snow belt region of Michigan\u27s Upper Peninsula, WADS is the first multi-modal dataset featuring dense point-wise labeled sequential LiDAR scans collected in severe winter weather. Over 26 TB of multi modal data has been collected of which over 7 GB of LiDAR point clouds (3.6 billion points) have been labeled (semanticKITTI format) and made available here. This outdoor dataset introduces falling snow and accumulated snow along with all the semanticKITTI classes. We believe this dataset will further AV tasks like semantic and panoptic segmentation, object detection and tracking, and localization and mapping in conditions of moderate to severe snow

The Winter Adverse Driving dataSet (WADS) - sequence 35

Collected in the snow belt region of Michigan\u27s Upper Peninsula, WADS is the first multi-modal dataset featuring dense point-wise labeled sequential LiDAR scans collected in severe winter weather. Over 26 TB of multi modal data has been collected of which over 7 GB of LiDAR point clouds (3.6 billion points) have been labeled (semanticKITTI format) and made available here. This outdoor dataset introduces falling snow and accumulated snow along with all the semanticKITTI classes. We believe this dataset will further AV tasks like semantic and panoptic segmentation, object detection and tracking, and localization and mapping in conditions of moderate to severe snow