Autonomous navigation in off-road conditions requires an accurate estimation of terrain traversability. However, traversability estimation in unstructured environments is subject to high uncertainty due to the variability of numerous factors that influence vehicle-terrain interaction. Consequently, it is challenging to obtain a generalizable model that can accurately predict traversability in a variety of environments. This paper presents METAVerse, a meta-learning framework for learning a global model that accurately and reliably predicts terrain traversability across diverse environments. We train the traversability prediction network to generate a dense and continuous-valued cost map from a sparse LiDAR point cloud, leveraging vehicle-terrain interaction feedback in a self-supervised manner. Meta-learning is utilized to train a global model with driving data collected from multiple environments, effectively minimizing estimation uncertainty. During deployment, online adaptation is performed to rapidly adapt the network to the local environment by exploiting recent interaction experiences. To conduct a comprehensive evaluation, we collect driving data from various terrains and demonstrate that our method can obtain a global model that minimizes uncertainty. Moreover, by integrating our model with a model predictive controller, we demonstrate that the reduced uncertainty results in safe and stable navigation in unstructured and unknown terrains.

Estimating the traversability of rough terrain is a critical task for an outdoor mobile robot. While classifying structured environment can be learned from large number of training data, it is an extremely difficult task to learn and estimate traversability of unstructured rough terrain. Moreover, in many cases information from a single sensor may not be sufficient for estimating traversability reliably in the absence of artificial landmarks such as lane markings or curbs. Our approach estimates traversability of the terrain and build a 2D probabilistic grid map online using 3D-LIDAR and camera. The combination of LIDAR and camera is favoured in many robotic application because they provide complementary information. Our approach assumes the data captured by these two sensors are independent and build separate traversability maps, each with information captured from one sensor. Traversability estimation with vision sensor autonomously collects training data and update classifier without human intervention as the vehicle traverse the terrain. Traversability estimation with 3D-LIDAR measures the slopes of the ground to predict the traversability. Two independently built probabilistic maps are fused using Bayes' rule to improve the detection performance. This is in contrast with other methods in which each sensor performs different tasks. We have implemented the algorithm on a UGV(Unmanned Ground Vehicle) and tested our approach on a rough terrain to evaluate the detection performance.

Object representation is useful for many computer vision tasks, such as object detection, recognition, and tracking. Computer vision tasks must handle situations where unknown objects appear and must detect and track some object which is not in the trained database. In such cases, the system must learn or, otherwise derive, descriptions of new objects. In this paper, we investigate creating a representation of previously unknown objects that newly appear in the scene. The representation creates a viewpoint-invariant and scale-normalized model approximately describing an unknown object with multimodal sensors. Those properties of the representation facilitate 3D tracking of the object using 2D-to-2D image matching. The representation has both benefits of an implicit model (referred to as a view-based model) and an explicit model (referred to as a shape-based model). Experimental results demonstrate the viability of the proposed representation and outperform the existing approaches for 3D-pose estimation.

In this paper we propose a hybrid vision-based SLAM and moving objects tracking (vSLAMMOT) approach. This approach tightly combines two key methods: a superpixel-based segmentation to detect moving objects and a Rao-Blackwellized Particle Filter to estimate a stereo-vision-based SLAM posterior. Most successful methods perform vision-based SLAM (vSLAM) and track moving objects independently. However, we pose both vSLAM and moving object tracking as a single correlated problem to leverage the performance. Our approach estimates the relative camera motion using the previous tracking result, and then detects moving objects from the estimated camera motion recursively. Moving superpixels are detected by a Markov Random Field (MRF) model which uses spatial and temporal information of the moving objects. We demonstrate the performance of the proposed approach for vSLAMMOT using both synthetic and real datasets and compare the performance with other methods.

In robotic hands design tendon driven systems have been considered for years. The main advantage is a small end effector inertia e.g. a light, small hand with high dynamics due to remote actuators. To protect the actuators from impact in unknown environments a compliant mechanism can be used. It absorbs energy during an impact or saves energy to enhance the joint dynamics. In this paper an antagonistic tendon mechanism is presented. It fits 38 times in the DLR Hand Arm System forearm and enables is adapted to the different finger joints and different tendon lengths. A magnetic sensor was developed for the force measurement of the tendons. Finally, the calibration and the robustness are demonstrated through a set of experiments.

Detecting the boundaries of objects is a key step in separating foreground objects from the background, which is useful for robotics and computer vision applications, such as object detection, recognition, and tracking. We propose a new method for detecting object boundaries using planar laser scanners (LIDARs) and, optionally, co-registered imagery. We formulate boundary detection as a classification problem, in which we estimate whether a boundary exists in the gap between two consecutive range measurements. Features derived from the LIDAR and imagery are used to train a support vector machine (SVM) classifier to label pairs of range measurements as boundary or non-boundary. We compare this approach to an existing boundary detection algorithm that uses dynamically adjusted thresholds. Experiments show that the new method performs better even when only LIDAR features are used, and additional improvement occurs when image-based features are included, too. The new algorithm performs better on difficult boundary cases, such as obliquely viewed objects.