Autonomous Off-road Navigation over Extreme Terrains with Perceptually-challenging Conditions

• URL: http://arxiv.org/abs/2101.11110v1
• Date: Tue, 26 Jan 2021 22:13:01 GMT
• Title: Autonomous Off-road Navigation over Extreme Terrains with Perceptually-challenging Conditions
• Authors: Rohan Thakker, Nikhilesh Alatur, David D. Fan, Jesus Tordesillas, Michael Paton, Kyohei Otsu, Olivier Toupet, Ali-akbar Agha-mohammadi
• Abstract summary: We propose a framework for resilient autonomous computation in perceptually challenging environments with mobility-stressing elements. We propose a fast settling algorithm to generate robust multi-fidelity traversability estimates in real-time. The proposed approach was deployed on multiple physical systems including skid-steer and tracked robots, a high-speed RC car and legged robots.
• Score: 7.514178230130502
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a framework for resilient autonomous navigation in perceptually challenging unknown environments with mobility-stressing elements such as uneven surfaces with rocks and boulders, steep slopes, negative obstacles like cliffs and holes, and narrow passages. Environments are GPS-denied and perceptually-degraded with variable lighting from dark to lit and obscurants (dust, fog, smoke). Lack of prior maps and degraded communication eliminates the possibility of prior or off-board computation or operator intervention. This necessitates real-time on-board computation using noisy sensor data. To address these challenges, we propose a resilient architecture that exploits redundancy and heterogeneity in sensing modalities. Further resilience is achieved by triggering recovery behaviors upon failure. We propose a fast settling algorithm to generate robust multi-fidelity traversability estimates in real-time. The proposed approach was deployed on multiple physical systems including skid-steer and tracked robots, a high-speed RC car and legged robots, as a part of Team CoSTAR's effort to the DARPA Subterranean Challenge, where the team won 2nd and 1st place in the Tunnel and Urban Circuits, respectively.

Related papers

• Real-Time Environment Condition Classification for Autonomous Vehicles [3.8514288339458718]
  We train a deep learning model to identify outdoor weather and dangerous road conditions. We achieve this by introducing an improved taxonomy and label hierarchy for a state-of-the-art adverse-weather dataset. We train RECNet, a deep learning model for the classification of environment conditions from a single RGB frame.
  arXiv  Detail & Related papers  (2024-05-29T17:29:55Z)
• RoadRunner -- Learning Traversability Estimation for Autonomous Off-road Driving [13.101416329887755]
  We present RoadRunner, a 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. We demonstrate the effectiveness of RoadRunner in enabling safe and reliable off-road navigation at high speeds in multiple real-world driving scenarios through unstructured desert environments.
  arXiv  Detail & Related papers  (2024-02-29T16:47:54Z)
• Floor extraction and door detection for visually impaired guidance [78.94595951597344]
  Finding obstacle-free paths in unknown environments is a big navigation issue for visually impaired people and autonomous robots. New devices based on computer vision systems can help impaired people to overcome the difficulties of navigating in unknown environments in safe conditions. In this work it is proposed a combination of sensors and algorithms that can lead to the building of a navigation system for visually impaired people.
  arXiv  Detail & Related papers  (2024-01-30T14:38:43Z)
• RSRD: A Road Surface Reconstruction Dataset and Benchmark for Safe and Comfortable Autonomous Driving [67.09546127265034]
  Road surface reconstruction helps to enhance the analysis and prediction of vehicle responses for motion planning and control systems. We introduce the Road Surface Reconstruction dataset, a real-world, high-resolution, and high-precision dataset collected with a specialized platform in diverse driving conditions. It covers common road types containing approximately 16,000 pairs of stereo images, original point clouds, and ground-truth depth/disparity maps.
  arXiv  Detail & Related papers  (2023-10-03T17:59:32Z)
• A real-time dynamic obstacle tracking and mapping system for UAV navigation and collision avoidance with an RGB-D camera [7.77809394151497]
  We propose a real-time dynamic obstacle tracking and mapping system for quadcopter obstacle avoidance using an RGB-D camera. Our methods can successfully track and represent obstacles in dynamic environments in real-time and safely avoid obstacles.
  arXiv  Detail & Related papers  (2022-09-17T05:32:33Z)
• Coupling Vision and Proprioception for Navigation of Legged Robots [65.59559699815512]
  We exploit the complementary strengths of vision and proprioception to achieve point goal navigation in a legged robot. We show superior performance compared to wheeled robot (LoCoBot) baselines. We also show the real-world deployment of our system on a quadruped robot with onboard sensors and compute.
  arXiv  Detail & Related papers  (2021-12-03T18:59:59Z)
• Learning High-Speed Flight in the Wild [101.33104268902208]
  We propose an end-to-end approach that can autonomously fly quadrotors through complex natural and man-made environments at high speeds. The key principle is to directly map noisy sensory observations to collision-free trajectories in a receding-horizon fashion. By simulating realistic sensor noise, our approach achieves zero-shot transfer from simulation to challenging real-world environments.
  arXiv  Detail & Related papers  (2021-10-11T09:43:11Z)
• NeBula: Quest for Robotic Autonomy in Challenging Environments; TEAM CoSTAR at the DARPA Subterranean Challenge [105.27989489105865]
  This paper presents and discusses algorithms, hardware, and software architecture developed by the TEAM CoSTAR. The paper introduces our autonomy solution, referred to as NeBula (Networked Belief-aware Perceptual Autonomy).
  arXiv  Detail & Related papers  (2021-03-21T19:42:26Z)
• Vision-Based Mobile Robotics Obstacle Avoidance With Deep Reinforcement Learning [49.04274612323564]
  Obstacle avoidance is a fundamental and challenging problem for autonomous navigation of mobile robots. In this paper, we consider the problem of obstacle avoidance in simple 3D environments where the robot has to solely rely on a single monocular camera. We tackle the obstacle avoidance problem as a data-driven end-to-end deep learning approach.
  arXiv  Detail & Related papers  (2021-03-08T13:05:46Z)
• STEP: Stochastic Traversability Evaluation and Planning for Safe Off-road Navigation [9.423950528323893]
  We propose an approach for assessing traversability and planning a safe, feasible, and fast trajectory in real-time. Our approach relies on: 1) rapid uncertainty-aware mapping and traversability evaluation, 2) tail risk assessment using the Conditional Value-at-Risk (CVaR), and 3) efficient risk and constraint-aware kinodynamic motion planning. We analyze our method in simulation and validate its efficacy on wheeled and legged robotic platforms exploring extreme terrains including an underground lava tube.
  arXiv  Detail & Related papers  (2021-03-04T04:24:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.