Navigating the Landscape for Real-time Localisation and Mapping for Robotics and Virtual and Augmented Reality

Abstract

Visual understanding of 3D environments in real-time, at low power, is a huge computational challenge. Often referred to as SLAM (Simultaneous Localisation and Mapping), it is central to applications spanning domestic and industrial robotics, autonomous vehicles, virtual and augmented reality. This paper describes the results of a major research effort to assemble the algorithms, architectures, tools, and systems software needed to enable delivery of SLAM, by supporting applications specialists in selecting and configuring the appropriate algorithm and the appropriate hardware, and compilation pathway, to meet their performance, accuracy, and energy consumption goals. The major contributions we present are (1) tools and methodology for systematic quantitative evaluation of SLAM algorithms, (2) automated, machine-learning-guided exploration of the algorithmic and implementation design space with respect to multiple objectives, (3) end-to-end simulation tools to enable optimisation of heterogeneous, accelerated architectures for the specific algorithmic requirements of the various SLAM algorithmic approaches, and (4) tools for delivering, where appropriate, accelerated, adaptive SLAM solutions in a managed, JIT-compiled, adaptive runtime context.

arXiv commentProceedings of the IEEE 2018

References in corpus (7)

• ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras
• Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age
• Informed RRT*: Optimal Sampling-based Path Planning Focused via Direct Sampling of an Admissible Ellipsoidal Heuristic
• Introducing SLAMBench, a performance and accuracy benchmarking methodology for SLAM
• InteriorNet: Mega-scale Multi-sensor Photo-realistic Indoor Scenes Dataset
• Comparative Design Space Exploration of Dense and Semi-Dense SLAM
• SLAMBench2: Multi-Objective Head-to-Head Benchmarking for Visual SLAM

Cited by in corpus (5)

• Semantics for Robotic Mapping, Perception and Interaction: A Survey
• Good Feature Matching: Towards Accurate, Robust VO/VSLAM with Low Latency
• Image-Based Trajectory Tracking through Unknown Environments without Absolute Positioning
• Visual Inertial Odometry using Focal Plane Binary Features (BIT-VIO)
• Closed-Loop Benchmarking of Stereo Visual-Inertial SLAM Systems: Understanding the Impact of Drift and Latency on Tracking Accuracy