Robust feature matching forms the backbone for most Visual Simultaneous Localization and Mapping (vSLAM), visual odometry, 3D reconstruction, and Structure from Motion (SfM) algorithms. However, recovering feature matches from texture-poor scenes is a major challenge and still remains an open area of research. In this paper, we present a Stereo Visual Odometry (StereoVO) technique based on point and line features which uses a novel feature-matching mechanism based on an Attention Graph Neural Network that is designed to perform well even under adverse weather conditions such as fog, haze, rain, and snow, and dynamic lighting conditions such as nighttime illumination and glare scenarios. We perform experiments on multiple real and synthetic datasets to validate our method's ability to perform StereoVO under low-visibility weather and lighting conditions through robust point and line matches. The results demonstrate that our method achieves more line feature matches than state-of-the-art line-matching algorithms, which when complemented with point feature matches perform consistently well in adverse weather and dynamic lighting conditions.

Light Detection And Rangings (LiDARs) have been widely adopted to modern self-driving vehicles, providing 3D information of the scene and surrounding objects. However, adverser weather conditions still pose significant challenges to LiDARs since point clouds captured during snowfall can easily be corrupted. The resulting noisy point clouds degrade downstream tasks such as mapping. Existing works in de-noising point clouds corrupted by snow are based on nearest-neighbor search, and thus do not scale well with modern LiDARs which usually capture 100k or more points at 10Hz. In this paper, we introduce an unsupervised de-noising algorithm, LiSnowNet, running 52 x faster than the state-of-the-art methods while achieving superior performance in de-noising. Unlike previous methods, the proposed algorithm is based on a deep convolutional neural network and can be easily deployed to hardware accelerators such as GPUs. In addition, we demonstrate how to use the proposed method for mapping even with corrupted point clouds.

Point cloud analysis is an area of increasing interest due to the development of 3D sensors that are able to rapidly measure the depth of scenes accurately. Unfortunately, applying deep learning techniques to perform point cloud analysis is non-trivial due to the inability of these methods to generalize to unseen rotations. To address this limitation, one usually has to augment the training data, which can lead to extra computation and require larger model complexity. This paper proposes a new neural network called the Aligned Edge Convolutional Neural Network (AECNN) that learns a feature representation of point clouds relative to Local Reference Frames (LRFs) to ensure invariance to rotation. In particular, features are learned locally and aligned with respect to the LRF of an automatically computed reference point. The proposed approach is evaluated on point cloud classification and part segmentation tasks. This paper illustrates that the proposed technique outperforms a variety of state of the art approaches (even those trained on augmented datasets) in terms of robustness to rotation without requiring any additional data augmentation.

Risk assessment to quantify the danger associated with taking a certain action is critical to navigating safely through crowded urban environments during autonomous driving. Risk assessment and subsequent planning is usually done by first tracking and predicting trajectories of other agents, such as vehicles and pedestrians, and then choosing an action to avoid future collisions. However, few existing risk assessment algorithms handle occlusion and other sensory limitations effectively. One either assesses the risk in the worst-case scenario and thus makes the ego vehicle overly conservative, or predicts as many hidden agents as possible and thus makes the computation intensive. This paper explores the possibility of efficient risk assessment under occlusion via both forward and backward reachability. The proposed algorithm can not only identify the location of risk-inducing factors, but can also be used during motion planning. The proposed method is evaluated on various four-way highly occluded intersections with up to five other vehicles in the scene. Compared with other risk assessment algorithms, the proposed method shows better efficiency, meaning that the ego vehicle reaches the goal at a higher speed. In addition, it also lowers the median collision rate by 7.5× when compared to state of the art techniques.

In this paper, we report on the development of the clock-torqued rolling spring loaded inverted pendulum (CTR-SLIP) model. The new model, which adds clock-based torque control on the leg orientation of the previously developed R-SLIP model, has two advantages: first, regulating the model to follow its passive dynamic running (i.e., at a fixed point) significantly increases the model's basin of attraction; and second, formulating the model closer to the empirical robot enables the model to serve as the transient and steady-state running template of the robot as the anchor. These features enable the model/robot to perform speed transition from one fixed point profile to another, and the experimental validation confirms that the robot can successfully transition between two running speeds bidirectionally. The achievement of variable-speed running by the proposed method has a unique merit-it is purely model-based, and there is no need for further tuning, optimization, or learning processes. Regarding the robot, the proposed strategy only requires it to have simple position control to regulate its leg orientations, and there is no need for other sensory modules to provide information for feedback.