DeepICP: An End-to-End Deep Neural Network for 3D Point Cloud Registration

We present DeepICP - a novel end-to-end learning-based 3D point cloud registration framework that achieves comparable registration accuracy to prior state-of-the-art geometric methods. Different from other keypoint based methods where a RANSAC procedure is usually needed, we implement the use of various deep neural network structures to establish an end-to-end trainable network. Our keypoint detector is trained through this end-to-end structure and enables the system to avoid the inference of dynamic objects, leverages the help of sufficiently salient features on stationary objects, and as a result, achieves high robustness. Rather than searching the corresponding points among existing points, the key contribution is that we innovatively generate them based on learned matching probabilities among a group of candidates, which can boost the registration accuracy. Our loss function incorporates both the local similarity and the global geometric constraints to ensure all above network designs can converge towards the right direction. We comprehensively validate the effectiveness of our approach using both the KITTI dataset and the Apollo-SouthBay dataset. Results demonstrate that our method achieves comparable or better performance than the state-of-the-art geometry-based methods. Detailed ablation and visualization analysis are included to further illustrate the behavior and insights of our network. The low registration error and high robustness of our method makes it attractive for substantial applications relying on the point cloud registration task.Comment: 10 pages, 6 figures, 3 tables, typos corrected, experimental results updated, accepted by ICCV 201

PELATIHAN PENERAPAN SISTEM TERBENAM DI SMP ALAM AR-RIDHO KOTA SEMARANG

Sistem Terbenam (Embedded Sistem) merupakan salah satu dasar dari Internet of Thing(IoT). Sistem Terbenam merupakan sebuah sistem (rangkaian elektronik) digital yangmerupakan bagian dari Sebuah sistem yang lebih besar, yang biasanya bukan berupa sistemelektronik. Secara umum, sistem Terbenam dirancang untuk aplikasi tertentu. SekolahAlam Ar-Ridho merupakan sekolah yang berbasis pada explorasi alam sebagai bahan pendidikan dengan konsep penelitian dasar. Pada sekolah ini, siswa dididik memanfaatkanalam sebagai media penelitian dan penggalian ide. Tujuan dari pengabdian masyarakat iniadalah menegnalkan Sistem Terbenam yang dapat menunjang penelitian dan penggalian ide oleh sekolah Alam Ar-Ridho untuk membantu dalam pengembangan proses belajarmengajar pada sekolah tersebut. Diharapkan dengan adanya pengenalan Sistem Terbenam,siswa dapat bereksplorasi lebih jauh dengan mempelajari dan memantau pertumbuhantanaman yang ada di lingkungan sekolah. Metode yang digunakan terdiri dari 4 tahap, yaitu(1) persiapan awal, (2) Analisis permasalahan mitra & penyusunan materi, (3) pelatihan danpendampingan, dan (4) Evaluasi. Kegiatan ini direncanakan dilaksanakan selama 4 bulanbertempat di Politeknik Negeri Semarang dan SMP Alam Ar Ridho. Kegiatan ini akanmemberikan pengalaman mengenai penggunaan alat dan Sistem Terbenam

Characterizing self-driving tasks in general-purpose architectures

Autonomous Vehicles (AVs) have the potential to radically change the automotive industry. How- ever, computing solutions for AVs have to meet severe performance constraints to guarantee a safe driving experience. Current solutions either exhibit high cost or fail to meet the stringent latency constraints. Therefore, the popularization of AVs requires a low-cost yet effective computing sys- tem. Understanding the sources of latency is key in order to improve autonomous driving systems. Here, we present a detailed characterization of Autoware, a modern self-driving car system. We analyze the performance of the different components and leverage hardware counters to identify the main bottlenecks.This work has been supported by the the CoCoUnit ERC Advanced Grant of the EU’s Horizon 2020 program (grant No 833057), the Spanish State Research Agency under grant PID2020-113172RB-I00 (AEI/FEDER, EU), the ICREA Academia program, and the grant 2020 FPI-UPC_033.Peer ReviewedPostprint (published version

The Reach-Avoid Problem for Constant-Rate Multi-Mode Systems

A constant-rate multi-mode system is a hybrid system that can switch freely among a finite set of modes, and whose dynamics is specified by a finite number of real-valued variables with mode-dependent constant rates. Alur, Wojtczak, and Trivedi have shown that reachability problems for constant-rate multi-mode systems for open and convex safety sets can be solved in polynomial time. In this paper, we study the reachability problem for non-convex state spaces and show that this problem is in general undecidable. We recover decidability by making certain assumptions about the safety set. We present a new algorithm to solve this problem and compare its performance with the popular sampling based algorithm rapidly-exploring random tree (RRT) as implemented in the Open Motion Planning Library (OMPL).Comment: 26 page

Learning Based High-Level Decision Making for Abortable Overtaking in Autonomous Vehicles

Autonomous vehicles are a growing technology that aims to enhance safety, accessibility, efficiency, and convenience through autonomous maneuvers ranging from lane change to overtaking. Overtaking is one of the most challenging maneuvers for autonomous vehicles, and current techniques for autonomous overtaking are limited to simple situations. This paper studies how to increase safety in autonomous overtaking by allowing the maneuver to be aborted. We propose a decision-making process based on a deep Q-Network to determine if and when the overtaking maneuver needs to be aborted. The proposed algorithm is empirically evaluated in simulation with varying traffic situations, indicating that the proposed method improves safety during overtaking maneuvers. Furthermore, the approach is demonstrated in real-world experiments using the autonomous shuttle iseAuto.Comment: 11 pages, 16 figures. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

AUTONOMOUS VEHICLE SIMULATION WITH MULTI HUMAN DRIVING BEHAVIOR USING DEEP LEARNING

Advances in Autonomous Vehicle (AV) technology have made this topic popular in recent years, both large and small companies have started to develop this AV technology. Apart from large companies, several researchers are also interested in developing this technology. However, due to cost constraints and security issues, the researchers developed AV using a computer simulation approach. The main objective of this paper is to create a simulation (AV). The simulation was created using Udacity self-driving-car from Unity 3D. The first step we took was to take a dataset in the form of images from a number of participants by manually driving a car in a simulation to get Human-driving-behavior. After the dataset is obtained, the AV model formation process will then be carried out using the deep learning method of the Convolutional Neural Network algorithm. In this research, a good AV simulation has been successfully made, the car can run perfectly following the track without experiencing a collision or going off the track. From the results of the testing carried out, the model that was built got pretty good results where the accuracy was 71% and the loss was 0.0165