Detection And Tracking Of Moving Objects Research Articles

Risk-Aware Trajectory Sampling for Quadrotor Obstacle Avoidance in Dynamic Environments

Obstacle avoidance of quadrotors in dynamic environments is still a very open problem. Current works commonly leverage traditional static maps to represent static obstacles, and the detection and tracking of moving objects (DATMO) method to model dynamic obstacles separately. The detection module requires pre-training, and the dynamic obstacles can only be modeled with certain shapes, such as cylinders or ellipsoids. This work utilizes the dual-structure particle-based (DSP) dynamic occupancy map to represent the arbitrarily-shaped static obstacles and dynamic obstacles simultaneously, and proposes an efficient risk-aware sampling-based local trajectory planner to realize safe flights. The trajectory is planned by sampling motion primitives generated in the state space. Each motion primitive is divided into two phases: a short-term phase with a strict risk limitation and a relatively long-term phase designed to avoid high-risk regions. The risk is evaluated with the predicted particle-form future occupancy status, considering the time dimension. With an approach of splitting from and merging to an arbitrary global trajectory, the planner can also be used in tasks with preplanned global trajectories. Comparison experiments show that the obstacle avoidance system composed of the DSP map and our planner performs the best in dynamic environments. In real-world tests, our quadrotor reaches a speed of 6 m/s with the motion capture system and 2.5 m/s with everything running on a low-price single-board computer.

MAPPING WITHOUT DYNAMIC: ROBUST LiDAR-SLAM FOR UGV MOBILE MAPPING IN DYNAMIC ENVIRONMENTS

Abstract. Detection And Tracking of Moving Objects (DATMO) is essential and necessary for mobile mapping system to generate clean and accurate point clouds maps since dynamic targets in real-world scenarios will deteriorate the performance of whole system. In this research, a robust LiDAR-SLAM system is presented incorporated with a real-time dynamic objects removal module to improve the accuracy of 6 DOF pose estimation and precision of maps. The key idea of the proposed method is to efficiently cluster the sparse point clouds of moving objects and then track them independently so as to relieve their influence on the odometry and mapping results. In the back-end, in order to further refine the point clouds maps, a valid probabilistic map fusion method is performed based on the free-space theory. We have evaluated our system on the dataset collected from daily crowded environments full of moving objects, providing competitive results with the state-of-the-art system both on the pose estimation and point cloud mapping.

Moving Object Detection and Tracking Based on Interaction of Static Obstacle Map and Geometric Model-Free Approachfor Urban Autonomous Driving

Detection and tracking of moving objects (DATMO) in an urban environment using Light Detection and Ranging (LiDAR) is a major challenge for autonomous vehicles due to sparse point cloud, multiple moving directions, various traffic participants, and computational load. To address the complexity of this issue, this study presents a novel model-free approach for DATMO using 2D LiDAR implemented on autonomous vehicles. The approach has been used to classify moving points in the point cloud using the predicted Static Obstacle Map (SOM) generated via interaction between Geometric Model-Free Approach (GMFA) and SOM, and estimates the state of each moving object via GMFA. The motion of each point represented by the state of moving objects updates the SOM. The interaction between GMFA and SOM estimates the correspondence between consecutive point clouds in real-time. The proposed approach has been evaluated via RT range and labeled dataset. The accuracy of estimation of the yaw angle and the velocity of a moving vehicle has been quantitatively evaluated using the RT-range. The performance is significantly improved compared with the geometric model-based tracking (MBT). The estimation of the yaw angle, which has a significant effect on the cut-in/cut-out intention of the target vehicle, is shown to be remarkably improved. Based on the evaluation of the labeled dataset, false-positive and false-negative features are suppressed more than MBT.

A mobile ground-based radar sensor for detection and tracking of moving objects

The detection and tracking of moving objects (DATMO) in an outdoor environment from a mobile robot are difficult tasks because of the wide variety of dynamic objects. A reliable discrimination of mobile and static detections without any prior knowledge is often conditioned by a good position estimation obtained using Global Positionning System/Differential Global Positioning System (GPS/DGPS), proprioceptive sensors, inertial sensors or even the use of Simultaneous Localization and Mapping (SLAM) algorithms. In this article a solution of the DATMO problem is presented to perform this task using only a microwave radar sensor. Indeed, this sensor provides images of the environment from which Doppler information can be extracted and interpreted in order to obtain not only velocities of detected objects but also the robot's own velocity.

Grid-based localization and local mapping with moving object detection and tracking

We present a real-time algorithm for simultaneous localization and local mapping (local SLAM) with detection and tracking of moving objects (DATMO) in dynamic outdoor environments from a moving vehicle equipped with a laser scanner, short-range radars and odometry. To correct the vehicle odometry we introduce a new fast implementation of incremental scan matching method that can work reliably in dynamic outdoor environments. After obtaining a good vehicle localization, the map surrounding of the vehicle is updated incrementally and moving objects are detected without a priori knowledge of the targets. Detected moving objects are finally tracked by a Multiple Hypothesis Tracker (MHT) coupled with an adaptive Interacting Multiple Model (IMM) filter. The experimental results on datasets collected from different scenarios such as: urban streets, country roads and highways demonstrate the efficiency of the proposed algorithm.

Simultaneous Localization, Mapping and Moving Object Tracking

Simultaneous localization, mapping and moving object tracking (SLAMMOT) involves both simultaneous localization and mapping (SLAM) in dynamic environments and detecting and tracking these dynamic objects. In this paper, a mathematical framework is established to integrate SLAM and moving object tracking. Two solutions are described: SLAM with generalized objects, and SLAM with detection and tracking of moving objects (DATMO). SLAM with generalized objects calculates a joint posterior over all generalized objects and the robot. Such an approach is similar to existing SLAM algorithms, but with additional structure to allow for motion modeling of generalized objects. Unfortunately, it is computationally demanding and generally infeasible. SLAM with DATMO decomposes the estimation problem into two separate estimators. By maintaining separate posteriors for stationary objects and moving objects, the resulting estimation problems are much lower dimensional than SLAM with generalized objects. Both SLAM and moving object tracking from a moving vehicle in crowded urban areas are daunting tasks. Based on the SLAM with DATMO framework, practical algorithms are proposed which deal with issues of perception modeling, data association, and moving object detection. The implementation of SLAM with DATMO was demonstrated using data collected from the CMU Navlab11 vehicle at high speeds in crowded urban environments. Ample experimental results shows the feasibility of the proposed theory and algorithms.

Feature extraction for moving objects tracking system in indoor environments

In this paper a feature extraction and classification methodology for a laser based detection and tracking of moving objects (DATMO) system in indoors environments is presented. The sensory perception is based in a SICK Laser Measurement System (LMS). An adaptation of the Hough Transform is used in the feature extraction procedure to interpret scanned segments, as primitive features, defined by geometric evidence (points, lines, circles and blobs), and high-level features, generally referred to as landmarks (corners, columns, doors, etc.). The classification system uses features data, some heuristic rules, and data from a Kalman filter based tracking system to classify multiple objects. Real and simulated results are presented to verify the effectiveness of the proposed DATMO system in unknown environments with multiple moving objects.