2,697 research outputs found
Autonomous Trail Following
Trails typically lack standard markers that characterize roadways. Nevertheless, trails are useful for off-road navigation. Here, trail following problem is approached by identifying the deviation of the robot from the heading direction of the trail by fine-tuning a pre-trained Inception-V3 [1] network. Key questions considered in this work include the required number, nature and geometry of the cameras and how trail types -- encoded in pre-existing maps -- can be exploited in addressing this task. Through evaluation of representative image datasets and on-robot testing we found: (i) that although a single camera cannot estimate angular deviation from the heading direction, but it can reliably detect that the robot is, or is not, following the trail; (ii) that two cameras pointing towards the left and the right can be used to estimate heading reliably within a differential framework; (iii) that trail nature is a useful tool for training networks for different trail types
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
TerrainNet: Visual Modeling of Complex Terrain for High-speed, Off-road Navigation
Effective use of camera-based vision systems is essential for robust
performance in autonomous off-road driving, particularly in the high-speed
regime. Despite success in structured, on-road settings, current end-to-end
approaches for scene prediction have yet to be successfully adapted for complex
outdoor terrain. To this end, we present TerrainNet, a vision-based terrain
perception system for semantic and geometric terrain prediction for aggressive,
off-road navigation. The approach relies on several key insights and practical
considerations for achieving reliable terrain modeling. The network includes a
multi-headed output representation to capture fine- and coarse-grained terrain
features necessary for estimating traversability. Accurate depth estimation is
achieved using self-supervised depth completion with multi-view RGB and stereo
inputs. Requirements for real-time performance and fast inference speeds are
met using efficient, learned image feature projections. Furthermore, the model
is trained on a large-scale, real-world off-road dataset collected across a
variety of diverse outdoor environments. We show how TerrainNet can also be
used for costmap prediction and provide a detailed framework for integration
into a planning module. We demonstrate the performance of TerrainNet through
extensive comparison to current state-of-the-art baselines for camera-only
scene prediction. Finally, we showcase the effectiveness of integrating
TerrainNet within a complete autonomous-driving stack by conducting a
real-world vehicle test in a challenging off-road scenario
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