Introduction to Low-cost and Low-consumption Optimization Schemes for Simultaneous Localization and Mapping

As an important technology required for intelligent devices such as autonomous driving and autonomous robots that can actively obtain environmental information, synchronous positioning and map construction technology has developed in response to practical demands and technological progress, and has given rise to a variety of algorithms with different efficiencies and consumption levels. However, the existing research related to SLAM is more focused on improving the performance of simultaneous localization and mapping algorithms in complex environments, while there are relatively few specialized optimizations for devices with lower computing or sensing capabilities. This paper mainly studies the existing solutions for reducing the requirements of synchronous positioning and map construction technology for the computing power or perception ability of devices, sorts out the relevant academic papers since 2009, and analyzes these papers through the review method. And from this, several optimization schemes for reducing the consumption of synchronous positioning and map construction algorithms have been sorted out, such as adding additional sensors like inertial measurement units, reducing the consumption of the search process through more efficient heuristic algorithms, lowering the data processing volume through dimensionality reduction, and reducing the recognition difficulty through strong features.