11 accepted papers
In order to navigate safely and reliably in off-road and unstructured environments, robots must detect anomalies that are out-of-distribution (OOD) with respect to the training data. We present an analysis-by-synthesis approach for pixel-wise anomaly detection without making any assumptions about th
While navigating unknown environments, robots rely primarily on proximate features for guidance in decision making, such as depth information from lidar to build a costmap, or local semantic information from images. The limited range over which these features can be used may result in poor robot beh
A key open challenge in off-road autonomy is that the traversability of terrain often depends on the vehicle's state. In particular, some obstacles are only traversable from some orientations. However, learning this interaction by encoding the angle of approach as a model input demands a large and d…
SLAM is an important capability for many autonomous systems, and modern LiDAR-based methods offer promising performance. However, for long duration missions, existing works that either take directly the full pointclouds or extracted features face key tradeoffs in accuracy and computational efficienc
Long-duration, off-road, autonomous missions require robots to continuously perceive their surroundings regardless of the ambient lighting conditions. Most existing autonomy systems heavily rely on active sensing, e.g., LiDAR, RADAR, and Time-of-Flight sensors, or use (stereo) visible light imaging
Self-supervised learning is a powerful approach for developing traversability models for off-road navigation, but these models often struggle with inputs unseen during training. Existing methods utilize techniques like evidential deep learning to quantify model uncertainty, helping to identify and a
Keyframes are LiDAR scans saved for future reference in Simultaneous Localization And Mapping (SLAM), but despite their central importance most algorithms leave choices of which scans to save and how to use them to wasteful heuristics. This work proposes two novel keyframe selection strategies for l
In order to navigate safely and reliably in novel environments, robots must estimate perceptual uncertainty when confronted with out-of-distribution (OOD) obstacles not seen in training data. We present a method to accurately estimate pixel-wise uncertainty in semantic segmentation without requiring…
A key challenge in off-road navigation is that even visually similar terrains or ones from the same semantic class may have substantially different traction properties. Existing work typically assumes no wheel slip or uses the expected traction for motion planning, where the predicted trajectories p…
In this paper we perform an extensive experimental evaluation of three planar target based 3D-LIDAR camera calibration algorithms, on a sensor suite consisting multiple 3D-LIDARs and cameras, assessing their robustness to random initialization and by using metrics like Mean Line Re-projection Error…
We present a system for determining a global solution for the relative poses between multiple sensors with different modalities and varying fields of view. The final calibration result produces a tree of transforms rooted at a single sensor that allows the fusion of the sensor streams into a shared…