Regressed Terrain Traversability Cost for Autonomous Navigation Based on Image Textures

Bekhti, Mohammed Abdessamad; Kobayashi, Yuichi

doi:10.3390/app10041195

Cited by 17 publications

(9 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the past, several works have been proposed for the in-situ classification of terrain types, based exclusively on proprioceptive data while actually experiencing the traverse on the (potentially dangerous) terrain [ 88 , 89 ]. Among the works reported in the present survey, a recent and more frequently adopted approach consists of using proprioceptive data during training as well as for data labeling [ 34 , 35 , 66 ]. This approach has already proved to provide better classification and regression results compared to those methods based on exteroceptive data only, although only the latter are used at deployment time.…”

Section: Discussionmentioning

confidence: 99%

“…In a similar fashion, ekhti and Kobayashi [ 35 ] train a Gaussian process regressor (GPR) in order to predict vehicle vibration (as a measure of terrain traversability) while moving over the terrain, combined with terrain textures features detected by processing images from an on-board RGB camera. Also in this case, the regressor is trained with proprioceptive data (i.e., accelerometer data) acquired during the traversal, whereas on-line traversal cost regression is based on the incoming RGB image only.…”

Section: Terrain Traversability Analysismentioning

confidence: 99%

See 1 more Smart Citation

Learning-Based Methods of Perception and Navigation for Ground Vehicles in Unstructured Environments: A Review

Guastella

Muscato

2020

Sensors

View full text Add to dashboard Cite

The problem of autonomous navigation of a ground vehicle in unstructured environments is both challenging and crucial for the deployment of this type of vehicle in real-world applications. Several well-established communities in robotics research deal with these scenarios such as search and rescue robotics, planetary exploration, and agricultural robotics. Perception plays a crucial role in this context, since it provides the necessary information to make the vehicle aware of its own status and its surrounding environment. We present a review on the recent contributions in the robotics literature adopting learning-based methods to solve the problem of environment perception and interpretation with the final aim of the autonomous context-aware navigation of ground vehicles in unstructured environments. To the best of our knowledge, this is the first work providing such a review in this context.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Terrain Traversability Analysismentioning

confidence: 99%

Learning-Based Methods of Perception and Navigation for Ground Vehicles in Unstructured Environments: A Review

Guastella

Muscato

2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Terrain traversability analysis can be referred to as the problem of estimating the difficulty of driving through a terrain for a ground vehicle [18]. Bekhti et al use terrain images and acceleration signals to train a Gaussian process regressor in order to predict vibrations using only image texture features [19]. Maturana et al propose a real-time mapping strategy that provides a 2.5D grid map centered on the vehicle frame, encoding both geometry and semantic information of the environment [20].…”

Section: Related Workmentioning

confidence: 99%

Proactive Anomaly Detection for Robot Navigation With Multi-Sensor Fusion

Sivakumar

Chowdhary

et al. 2022

IEEE Robot. Autom. Lett.

View full text Add to dashboard Cite

Despite the rapid advancement of navigation algorithms, mobile robots often produce anomalous behaviors that can lead to navigation failures. The ability to detect such anomalous behaviors is a key component in modern robots to achieve high-levels of autonomy. Reactive anomaly detection methods identify anomalous task executions based on the current robot state and thus lack the ability to alert the robot before an actual failure occurs. Such an alert delay is undesirable due to the potential damage to both the robot and the surrounding objects. We propose a proactive anomaly detection network (PAAD) for robot navigation in unstructured and uncertain environments. PAAD predicts the probability of future failure based on the planned motions from the predictive controller and the current observation from the perception module. Multi-sensor signals are fused effectively to provide robust anomaly detection in the presence of sensor occlusion as seen in field environments. Our experiments on field robot data demonstrates superior failure identification performance than previous methods, and that our model can capture anomalous behaviors in real-time while maintaining a low false detection rate in cluttered fields.Code is available at https://github.com/tianchenji/PAAD.

show abstract

“…Proprioceptive sensors that measure the internal state of the robot, e.g., acceleration [42,43], force [44,45], torque [45], vibration [44], are commonly used to generate supervisory signals for traversability as they directly reflect the physical robot-terrain interactions. Stavens and Thrun [42] generate the label of terrain roughness automatically from a vehicle's inertial measurements while driving.…”

Section: B Self-supervised Learning For Traversability Estimationmentioning

confidence: 99%

Multi-Task Learning for Scalable and Dense Multi-Layer Bayesian Map Inference

Gan¹,

Kim²,

Grizzle³

et al. 2021

Preprint

View full text Add to dashboard Cite

This paper presents a novel and flexible multi-task multi-layer Bayesian mapping framework with readily extendable attribute layers. The proposed framework goes beyond modern metric-semantic maps to provide even richer environmental information for robots in a single mapping formalism while exploiting existing inter-layer correlations. It removes the need for a robot to access and process information from many separate maps when performing a complex task and benefits from the correlation between map layers, advancing the way robots interact with their environments. To this end, we design a multi-task deep neural network with attention mechanisms as our front-end to provide multiple observations for multiple map layers simultaneously. Our back-end runs a scalable closedform Bayesian inference with only logarithmic time complexity. We apply the framework to build a dense robotic map including metric-semantic occupancy and traversability layers. Traversability ground truth labels are automatically generated from exteroceptive sensory data in a self-supervised manner. We present extensive experimental results on publicly available data sets and data collected by a 3D bipedal robot platform on the University of Michigan North Campus and show reliable mapping performance in different environments. Finally, we also discuss how the current framework can be extended to incorporate more information such as friction, signal strength, temperature, and physical quantity concentration using Gaussian map layers. The software for reproducing the presented results or running on customized data is made publicly available.

show abstract

Regressed Terrain Traversability Cost for Autonomous Navigation Based on Image Textures

Cited by 17 publications

References 32 publications

Learning-Based Methods of Perception and Navigation for Ground Vehicles in Unstructured Environments: A Review

Learning-Based Methods of Perception and Navigation for Ground Vehicles in Unstructured Environments: A Review

Proactive Anomaly Detection for Robot Navigation With Multi-Sensor Fusion

Multi-Task Learning for Scalable and Dense Multi-Layer Bayesian Map Inference

Contact Info

Product

Resources

About