Pedestrian Detection using Triple Laser Range Finders

Pedestrian detection is one of the important features in autonomous ground vehicle (AGV). It ensures the capability for safety navigation in urban environment. Therefore, the detection accuracy became a crucial part which leads to implementation using Laser Range Finder (LRF) for better data representation. In this study, an improved laser configuration and fusion technique is introduced by implementation of triple LRFs in two layers with Pedestrian Data Analysis (PDA) to recognize multiple pedestrians. The PDA integrates various features from feature extraction process for all clusters and fusion of multiple layers for better recognition. The experiments were conducted in various occlusion scenarios such as intersection, closed-pedestrian and combine scenarios. The analysis of the laser fusion and PDA for all scenarios showed an improvement of detection where the pedestrians were represented by various detection categories which solve occlusion issues when low numberof laser data were obtained

Laser Reflection Intensity and Multi-Layered Laser Range Finders for People Detection

Abstract-Successful detection of people is a basic requirement for a robot to achieve symbiosis in people's daily life. Specifically, a mobile robot designed to follow people needs to keep track of people's position through time, for it defines the robot's position and trajectory. In this work we introduce the usage of reflection intensity data of Laser Range Finders (LRF) arranged in multiple layers for people detection. We use supervised learning to train strong classifiers including intensity-based features. Concretely, we propose a calibration method for laser intensity and introduce new intensity-based features for people detection which are combined with range-based features in a strong classifier using supervised learning. We provide experimental results to evaluate the effectiveness of these features. This work is an step towards of our main research project of developing a social autonomous mobile robot acting as member of a people group

Deconvolutional networks for point-cloud vehicle detection and tracking in driving scenarios

© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Vehicle detection and tracking is a core ingredient for developing autonomous driving applications in urban scenarios. Recent image-based Deep Learning (DL) techniques are obtaining breakthrough results in these perceptive tasks. However, DL research has not yet advanced much towards processing 3D point clouds from lidar range-finders. These sensors are very common in autonomous vehicles since, despite not providing as semantically rich information as images, their performance is more robust under harsh weather conditions than vision sensors. In this paper we present a full vehicle detection and tracking system that works with 3D lidar information only. Our detection step uses a Convolutional Neural Network (CNN) that receives as input a featured representation of the 3D information provided by a Velodyne HDL-64 sensor and returns a per-point classification of whether it belongs to a vehicle or not. The classified point cloud is then geometrically processed to generate observations for a multi-object tracking system implemented via a number of Multi-Hypothesis Extended Kalman Filters (MH-EKF) that estimate the position and velocity of the surrounding vehicles. The system is thoroughly evaluated on the KITTI tracking dataset, and we show the performance boost provided by our CNN-based vehicle detector over a standard geometric approach. Our lidar-based approach uses about a 4% of the data needed for an image-based detector with similarly competitive results.Peer ReviewedPostprint (author's final draft

三次元レーザースキャナーを用いたインテリジェント自動ドア

従来の自動ドアの多くは、ドア上部に取り付けた近接センサを用いてドアへ進入する歩行者の検出を行っている。このセンサを使った自動ドアは歩行者が監視領域に進入するだけで動作するため、歩行者はドアに対して特別な操作をすることなくドアに入ることが出来る。このような自動ドアは開閉に力を必要としないため、日常生活において行動に制限がある人でもストレスなく使うことができる。しかし従来の自動ドアには反応が鈍い、誤動作する、といった問題点がある。反応が鈍い自動ドアとは、センサの監視領域の狭さや感度の悪さが原因でドアが開きづらい自動ドアのことで、歩行者のスムーズな通行を妨げている。誤動作というのは、ドアに入る意志がない歩行者に対して誤ってドアを開けてしまうことを指し、このような誤動作は開閉時の動力のほか、建物の空調の効率の面でも不経済である。センサの感度が低いことによる反応の鈍さと感度が不必要に高いことによる誤動作は相反する要求であり、従来のセンサを用いる限り同時に解決することはできない。そこで本研究では、ドアに接近する歩行者の人数や動きを観測し、通過する意思のある歩行者に対してのみ適切なタイミングでドアを開けるインテリジェント自動ドアの開発を目指す。具体的には、歩行者の位置情報が取得でき、環境の変化に強い三次元レーザースキャナーをドアセンサとして採用し、このセンサに対応したドアの開閉判定アルゴリズムを開発した。また、三次元レーザースキャナーを用いたドアセンサと従来の自動ドアに用いられている駆動系を組み合わせてインテリジェント自動ドアを構築し、実機実験による評価を通して以下の機能が実現されていることを確認した。・ドアに進入する歩行者にのみドアを開ける・歩行者の位置と速度を推定し、ドアの開きタイミングと開き速度を制御する・歩行者の人数を推定し、ドアの開き幅を制御する電気通信大学201