How Does It Feel? Self-Supervised Costmap Learning for Off-Road Vehicle Traversability

Castro, Mateo Guaman; Triest, Samuel; Wenshan, Wang,; Gregory, Jason M.; Sánchez, Félix; Rogers, John G.; Scherer, Sebastian

doi:10.1109/icra48891.2023.10160856

Cited by 18 publications

(3 citation statements)

References 32 publications

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“…Future work will address the limitations described in IV-D to make the simulator more realistic. We also aim to extend navigation components by adopting end-to-end learning [17]- [19], [39] and reinforcement learning techniques [28], [40], [41].…”

Section: Discussionmentioning

confidence: 99%

Endo Lab, Department of Mechanical Systems Engineering, Faculty of Engineering, Toyama Prefectural University

Endô¹

2022

Seikei kako

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As robotic navigation techniques in perception and planning advance, mobile robots increasingly venture into offroad environments involving complex traversability. However, selecting suitable planning methods remains a challenge due to their algorithmic diversity, as each offers unique benefits. To aid in algorithm design, we introduce BenchNav, an open-source PyTorch-based simulation platform for benchmarking off-road navigation with uncertain traversability. Built upon Gymnasium, BenchNav provides three key features: 1) a data generation pipeline for preparing synthetic natural environments, 2) built-in machine learning models for traversability prediction, and 3) consistent execution of path and motion planning across different algorithms. We show BenchNav's versatility through simulation examples in off-road environments, employing three representative planning algorithms from different domains. https://github.com/masafumiendo/benchnav *This work was partially supported by JSPS KAKENHI Grant Number JP22J22731.1 M. Endo and G. Ishigami are with the Space Robotics Group,

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Section: Discussionmentioning

confidence: 99%

Endo Lab, Department of Mechanical Systems Engineering, Faculty of Engineering, Toyama Prefectural University

Endô¹

2022

Seikei kako

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“…Methods operating in a self-supervised manner aim to overcome this limitation by generating a training signal without relying on manual annotation. Instead they exploit information from other sensor modalities (Brooks and Iagnemma, 2012;Otsu et al, 2016;Castro et al, 2023;Seo et al, 2023;Higa et al, 2019;Meng et al, 2023;Zürn et al, 2021;Sathyamoorthy et al, 2022), or the interaction of the robot with the environment (Richter and Roy, 2017;Seo et al, 2022;Frey et al, 2023;Ahtiainen et al, 2017;Gasparino et al, 2022;Cai et al, 2022;Xue et al, 2023b;Sathyamoorthy et al, 2022;Cai et al, 2023;Jung et al, 2023). The generated supervision signal allows training a model that predicts a look-ahead estimate of the terrain, all without requiring the robot to be near to or interact with the terrain.…”

Section: Traversability From Self-supervisionmentioning

confidence: 99%

“…(Higa et al, 2019) directly estimate driving energy for rovers from images using the measured energy consumption. (Castro et al, 2023) adapt the proprioception-based pseudo labels by measuring vibration data, which is used as the network input to predict a traversability grid map from a colorized elevation map. (Richter and Roy, 2017) proposed to use anomaly detection to predict safe image regions for indoor navigation with a wheeled robot.…”

Section: Traversability From Self-supervisionmentioning

confidence: 99%

Locomotion Policy Guided Traversability Learning using Volumetric Representations of Complex Environments

Frey¹,

Hoeller²,

Khattak³

et al. 2022

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Autonomous navigation at high speeds in off-road environments necessitates robots to comprehensively understand their surroundings using onboard sensing only. The extreme conditions posed by the off-road setting can cause degraded camera image quality due to poor lighting and motion blur, as well as limited sparse geometric information available from LiDAR sensing when driving at high speeds. In this work, we present RoadRunner, a novel framework capable of predicting terrain traversability and an elevation map directly from camera and LiDAR sensor inputs. RoadRunner enables reliable autonomous navigation, by fusing sensory information, handling of uncertainty, and generation of contextually informed predictions about the geometry and traversability of the terrain while operating at low latency. In contrast to existing methods relying on classifying handcrafted semantic classes and using heuristics to predict traversability costs, our method is trained end-to-end in a self-supervised fashion. The RoadRunner network architecture builds upon popular sensor fusion network architectures from the autonomous driving domain, which embed LiDAR and camera information into a common Bird's Eye View perspective. Training is enabled by utilizing an existing traversability estimation stack to generate training data in hindsight in a scalable manner from realworld off-road driving datasets. Furthermore, RoadRunner improves the system latency by a factor of ∼ 4, from 500 ms to 140 ms, while improving the accuracy for traversability costs and elevation map predictions. We demonstrate the effectiveness of RoadRunner in enabling safe and reliable offroad navigation at high speeds in multiple real-world driving scenarios through unstructured desert environments.

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Motion planning for off‐road autonomous driving based on human‐like cognition and weight adaptation

Wang,

Gong,

Gong

et al. 2024

Journal of Field Robotics

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Driving in an off‐road environment is challenging for autonomous vehicles due to the complex and varied terrain. To ensure stable and efficient travel, the vehicle requires consideration and balancing of environmental factors, such as undulations, roughness, and obstacles, to generate optimal trajectories that can adapt to changing scenarios. However, traditional motion planners often utilize a fixed cost function for trajectory optimization, making it difficult to adapt to different driving strategies in challenging irregular terrains and uncommon scenarios. To address these issues, we propose an adaptive motion planner based on human‐like cognition and cost evaluation for off‐road driving. First, we construct a multilayer map describing different features of off‐road terrains, including terrain elevation, roughness, obstacle, and artificial potential field map. Subsequently, we employ a convolutional neural network‐long short‐term memory network to learn the trajectories planned by human drivers in various off‐road scenarios. Then, based on human‐like generated trajectories in different environments, we design a primitive‐based trajectory planner that aims to mimic human trajectories and cost weight selection, generating trajectories that are consistent with the dynamics of off‐road vehicles. Finally, we compute optimal cost weights and select and extend behavioral primitives to generate highly adaptive, stable, and efficient trajectories. We validate the effectiveness of the proposed method through experiments in a desert off‐road environment with complex terrain and varying road conditions. The experimental results show that the proposed human‐like motion planner has excellent adaptability to different off‐road conditions. It shows real‐time operation, greater stability, and more human‐like planning ability in diverse and challenging scenarios.

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How Does It Feel? Self-Supervised Costmap Learning for Off-Road Vehicle Traversability

Cited by 18 publications

References 32 publications

Endo Lab, Department of Mechanical Systems Engineering, Faculty of Engineering, Toyama Prefectural University

Endo Lab, Department of Mechanical Systems Engineering, Faculty of Engineering, Toyama Prefectural University

Locomotion Policy Guided Traversability Learning using Volumetric Representations of Complex Environments

Motion planning for off‐road autonomous driving based on human‐like cognition and weight adaptation

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