61 research outputs found
Probabilistic Traversability Model for Risk-Aware Motion Planning in Off-Road Environments
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 provide a poor
indication of the actual performance if the terrain traction has high
uncertainty. In contrast, this work proposes to analyze terrain traversability
with the empirical distribution of traction parameters in unicycle dynamics,
which can be learned by a neural network in a self-supervised fashion. The
probabilistic traction model leads to two risk-aware cost formulations that
account for the worst-case expected cost and traction. To help the learned
model generalize to unseen environment, terrains with features that lead to
unreliable predictions are detected via a density estimator fit to the trained
network's latent space and avoided via auxiliary penalties during planning.
Simulation results demonstrate that the proposed approach outperforms existing
work that assumes no slip or uses the expected traction in both navigation
success rate and completion time. Furthermore, avoiding terrains with low
density-based confidence score achieves up to 30% improvement in success rate
when the learned traction model is used in a novel environment.Comment: To appear in IROS23. Video and code:
https://github.com/mit-acl/mppi_numb
EVORA: Deep Evidential Traversability Learning for Risk-Aware Off-Road Autonomy
Traversing terrain with good traction is crucial for achieving fast off-road
navigation. Instead of manually designing costs based on terrain features,
existing methods learn terrain properties directly from data via
self-supervision, but challenges remain to properly quantify and mitigate risks
due to uncertainties in learned models. This work efficiently quantifies both
aleatoric and epistemic uncertainties by learning discrete traction
distributions and probability densities of the traction predictor's latent
features. Leveraging evidential deep learning, we parameterize Dirichlet
distributions with the network outputs and propose a novel uncertainty-aware
squared Earth Mover's distance loss with a closed-form expression that improves
learning accuracy and navigation performance. The proposed risk-aware planner
simulates state trajectories with the worst-case expected traction to handle
aleatoric uncertainty, and penalizes trajectories moving through terrain with
high epistemic uncertainty. Our approach is extensively validated in simulation
and on wheeled and quadruped robots, showing improved navigation performance
compared to methods that assume no slip, assume the expected traction, or
optimize for the worst-case expected cost.Comment: Under review. Journal extension for arXiv:2210.00153. Project
website: https://xiaoyi-cai.github.io/evora
ArtPlanner: Robust Legged Robot Navigation in the Field
Due to the highly complex environment present during the DARPA Subterranean
Challenge, all six funded teams relied on legged robots as part of their
robotic team. Their unique locomotion skills of being able to step over
obstacles require special considerations for navigation planning. In this work,
we present and examine ArtPlanner, the navigation planner used by team CERBERUS
during the Finals. It is based on a sampling-based method that determines valid
poses with a reachability abstraction and uses learned foothold scores to
restrict areas considered safe for stepping. The resulting planning graph is
assigned learned motion costs by a neural network trained in simulation to
minimize traversal time and limit the risk of failure. Our method achieves
real-time performance with a bounded computation time. We present extensive
experimental results gathered during the Finals event of the DARPA Subterranean
Challenge, where this method contributed to team CERBERUS winning the
competition. It powered navigation of four ANYmal quadrupeds for 90 minutes of
autonomous operation without a single planning or locomotion failure
Learning-on-the-Drive: Self-supervised Adaptation of Visual Offroad Traversability Models
Autonomous off-road driving requires understanding traversability, which
refers to the suitability of a given terrain to drive over. When offroad
vehicles travel at high speed (), they need to reason at long-range
(-) for safe and deliberate navigation. Moreover, vehicles often
operate in new environments and under different weather conditions. LiDAR
provides accurate estimates robust to visual appearances, however, it is often
too noisy beyond 30m for fine-grained estimates due to sparse measurements.
Conversely, visual-based models give dense predictions at further distances but
perform poorly at all ranges when out of training distribution. To address
these challenges, we present ALTER, an offroad perception module that
adapts-on-the-drive to combine the best of both sensors. Our visual model
continuously learns from new near-range LiDAR measurements. This
self-supervised approach enables accurate long-range traversability prediction
in novel environments without hand-labeling. Results on two distinct real-world
offroad environments show up to 52.5% improvement in traversability estimation
over LiDAR-only estimates and 38.1% improvement over non-adaptive visual
baseline.Comment: 8 page
Radar-Only Off-Road Local Navigation
Off-road robotics have traditionally utilized lidar for local navigation due
to its accuracy and high resolution. However, the limitations of lidar, such as
reduced performance in harsh environmental conditions and limited range, have
prompted the exploration of alternative sensing technologies. This paper
investigates the potential of radar for off-road local navigation, as it offers
the advantages of a longer range and the ability to penetrate dust and light
vegetation. We adapt existing lidar-based methods for radar and evaluate the
performance in comparison to lidar under various off-road conditions. We show
that radar can provide a significant range advantage over lidar while
maintaining accuracy for both ground plane estimation and obstacle detection.
And finally, we demonstrate successful autonomous navigation at a speed of 2.5
m/s over a path length of 350 m using only radar for ground plane estimation
and obstacle detection.Comment: 7 pages, 17 figures, ITSC 202
Unifying terrain awareness for the visually impaired through real-time semantic segmentation.
Navigational assistance aims to help visually-impaired people to ambulate the environment safely and independently. This topic becomes challenging as it requires detecting a wide variety of scenes to provide higher level assistive awareness. Vision-based technologies with monocular detectors or depth sensors have sprung up within several years of research. These separate approaches have achieved remarkable results with relatively low processing time and have improved the mobility of impaired people to a large extent. However, running all detectors jointly increases the latency and burdens the computational resources. In this paper, we put forward seizing pixel-wise semantic segmentation to cover navigation-related perception needs in a unified way. This is critical not only for the terrain awareness regarding traversable areas, sidewalks, stairs and water hazards, but also for the avoidance of short-range obstacles, fast-approaching pedestrians and vehicles. The core of our unification proposal is a deep architecture, aimed at attaining efficient semantic understanding. We have integrated the approach in a wearable navigation system by incorporating robust depth segmentation. A comprehensive set of experiments prove the qualified accuracy over state-of-the-art methods while maintaining real-time speed. We also present a closed-loop field test involving real visually-impaired users, demonstrating the effectivity and versatility of the assistive framework
Data-Driven Convex Approach to Off-road Navigation via Linear Transfer Operators
We consider the problem of optimal navigation control design for navigation
on off-road terrain. We use traversability measure to characterize the degree
of difficulty of navigation on the off-road terrain. The traversability measure
captures the property of terrain essential for navigation, such as elevation
map, terrain roughness, slope, and terrain texture. The terrain with the
presence or absence of obstacles becomes a particular case of the proposed
traversability measure. We provide a convex formulation to the off-road
navigation problem by lifting the problem to the density space using the linear
Perron-Frobenius (P-F) operator. The convex formulation leads to an
infinite-dimensional optimal navigation problem for control synthesis. The
finite-dimensional approximation of the infinite-dimensional convex problem is
constructed using data. We use a computational framework involving the Koopman
operator and the duality between the Koopman and P-F operator for the
data-driven approximation. This makes our proposed approach data-driven and can
be applied in cases where an explicit system model is unavailable. Finally, we
demonstrate the application of the developed framework for the navigation of
vehicle dynamics with Dubin's car model
Self-Supervised Traversability Prediction by Learning to Reconstruct Safe Terrain
Navigating off-road with a fast autonomous vehicle depends on a robust
perception system that differentiates traversable from non-traversable terrain.
Typically, this depends on a semantic understanding which is based on
supervised learning from images annotated by a human expert. This requires a
significant investment in human time, assumes correct expert classification,
and small details can lead to misclassification. To address these challenges,
we propose a method for predicting high- and low-risk terrains from only past
vehicle experience in a self-supervised fashion. First, we develop a tool that
projects the vehicle trajectory into the front camera image. Second, occlusions
in the 3D representation of the terrain are filtered out. Third, an autoencoder
trained on masked vehicle trajectory regions identifies low- and high-risk
terrains based on the reconstruction error. We evaluated our approach with two
models and different bottleneck sizes with two different training and testing
sites with a fourwheeled off-road vehicle. Comparison with two independent test
sets of semantic labels from similar terrain as training sites demonstrates the
ability to separate the ground as low-risk and the vegetation as high-risk with
81.1% and 85.1% accuracy
GrASPE: Graph based Multimodal Fusion for Robot Navigation in Unstructured Outdoor Environments
We present a novel trajectory traversability estimation and planning
algorithm for robot navigation in complex outdoor environments. We incorporate
multimodal sensory inputs from an RGB camera, 3D LiDAR, and robot's odometry
sensor to train a prediction model to estimate candidate trajectories' success
probabilities based on partially reliable multi-modal sensor observations. We
encode high-dimensional multi-modal sensory inputs to low-dimensional feature
vectors using encoder networks and represent them as a connected graph to train
an attention-based Graph Neural Network (GNN) model to predict trajectory
success probabilities. We further analyze the image and point cloud data
separately to quantify sensor reliability to augment the weights of the feature
graph representation used in our GNN. During runtime, our model utilizes
multi-sensor inputs to predict the success probabilities of the trajectories
generated by a local planner to avoid potential collisions and failures. Our
algorithm demonstrates robust predictions when one or more sensor modalities
are unreliable or unavailable in complex outdoor environments. We evaluate our
algorithm's navigation performance using a Spot robot in real-world outdoor
environments
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