Point cloud segmentation (PCS) aims to make per-point predictions, enabling robots and autonomous driving cars to understand their environments. The range image is a dense representation of a large-scale outdoor point cloud. Segmentation models built upon the range image commonly execute efficiently. However, the projection of the point cloud onto the range image inevitably leads to dropping points because multiple points could be projected onto the same image location, but only one point is kept. More importantly, it is challenging to assign correct predictions to the dropped points with different classes from the kept point class. Besides, existing post-processing methods, such as K -nearest neighbor ( K NN) search and kernel point convolution (KPConv), cannot be trained with the models in an end-to-end manner or cannot process varying-density outdoor point clouds well, thereby enabling the models to achieve sub-optimal performance. To alleviate this problem, we propose a trainable pointwise decoder module (PDM) as the post-processing approach, which gathers weighted features from the neighbors and then makes the final prediction for the query point. In addition, we introduce a virtual range image-guided copy-rotate-paste (VRCrop) strategy in data augmentation. VRCrop constrains the total number of points and eliminates undesirable artifacts in the augmented point cloud. Also, VRCrop is model-agnostic and can be easily employed in various PCS models. With PDM and VRCrop, existing range image-based segmentation models consistently surpass their counterparts on the SemanticKITTI, SemanticPOSS, and nuScenes datasets.

Point cloud segmentation (PCS) aims to classify each point in a point cloud. The task plays an important role in robotics and remote sensing. However, existing copy-paste and copy-rotate-paste augmentation techniques cause undesirable artifacts in the augmented point cloud and cannot effectively copy and paste interesting objects from the whole training dataset, bringing difficulty in training image-points fused models and leading to sub-optimal PCS performance. In this paper, we propose an improved virtual range image-guided copy-rotate-paste (VRCrop++) strategy and a global copy-rotate-paste (GCrop) technique. VRCrop++ and GCrop remove unwanted artifacts in the augmented point cloud by a simple but effective “point-to-patch” strategy in the pasting step. Besides, GCrop copies the objects globally with the reciprocal of the point distribution ratio and effectively pastes the objects considering the minimal overlapping region. Extensive experiments conducted on SemanticKITTI and SemanticPOSS datasets demonstrate that with VRCrop++ and GCrop, the existing range image-points fused models consistently surpass their counterparts.

This paper presents Kinetic Energy Difference (KED) as a metric for collision proximity. The calculation of KED for differentially driven robots is explained, along with an example obstacle avoidance algorithm that utilizes it. This example algorithm is computationally efficient and simulations show that it is capable of guiding robots with slow dynamics through narrow corridors.

Pseudo-omnidirectional robots with individually steerable wheels ofïer a good balance between mobility, robustness and load-carrying capacity. However, accurate synchronization of the wheels' steering and rolling speeds is necessary to prevent energy-loss, mechanical stress and wheel slippage due to actuator infighting. This has proven to be a complex problem, especially when the wheels are not fixed to the robot body but are instead connected to it via actuated chains. This paper presents a mathematically simple method consisting of closed-form equations for path following and synchronizing the steering and rolling speeds of planar robots with a variable or fixed footprint, while respecting the velocity limits of each wheel's steering and rolling actuators. The presented method is thoroughly explained and simulation results are presented to show its performance.

This paper describes the development of Morri, a multi purpose robot platform. The design of this robot includes mechanical, electrical and software development. Key features of the robot are modularity for multi-purpose applications, affordable size for outdoor and indoor operation, low cost, high performance, and easy to use and repair in field conditions. The main focus on software architecture development has been on creating fully-functional real-time architecture, where several algorithms and methods can be easily integrated as part of the system. As a result, this robot took part on M-Elrob outdoor robot competition in July 2008 and won ldquoCamp Securityrdquo scenario.

This paper presents the design of an all-terrain wheeled robotic vehicle. The robot is tele-supervised containing several semi-autonomous and autonomous functionalities. The human operator's role is more like giving directions and targets for the robot rather that directly driving the robot. Navigating in an uncharted and non-structured environment presents a great challenge for a mobile autonomous robot in terms of obstacle detection and route navigation. The outdoor weather conditions also pose several challenges for the configuration of the robot's sensors and mechanics. The focus on this paper is on the design of the sensor system and the mechanics of the robot.

This paper presents a software architecture for the network-transparent control of distributed robotic systems. The system consists of two main components: a generic and easily extensible CORBA-based interface to distributed services, and a high-level XML-based description language for specifying the behavior of the robots. The architecture makes it possible to create dynamically modifiable, extensible control software with ease. It is successfully utilized in implementing a coffee serving system in which the co-operation of two very different robots and two other distributed services are needed.