\u25cfGenerally, automotive-grade LiDARs work at 5 to 20Hz. For automotive use cases, sensors can travel 1 to 5m over each\u3000\u00a0 scanning<\/p>\n
\u25cfThis 1 to 5m can cause many issues, especially in some cases which require highly accurate localisation and mapping<\/p>\n
\u25cfUsing Kudan\u2019s GrandSLAM, the system can correct and update the position of the sensor with the frequency of fused IMU which can generally run-up to the order of tens kHz, and compress the latency of position recognition less than ten micro sec<\/p>\n
\u25cfThis enables system output with practically zero lag for vehicle localisation and minimizes the real-time tracking accuracy into less than several cms<\/p>\n
Benefits of Zerolag tracking<\/strong><\/span><\/p>\n \u25cfVehicle\/ robot localisation can work even the sensors move with high speed and quick turns. This can contribute to highly accurate and real-time localisation for automotive, drones, aircrafts<\/p>\n \u25cfThe algorithm can save processing consumption with keeping the output frequency and latency, which enables the algorithms working on lower graded processors and\/ or providing more processing power for other algorithms (e.g., object recognition, navigation)<\/p>\n Example of Zerolag tracking<\/strong><\/span><\/p>\n \u25cfWe have run GrandSLAM on general automotive-grade LiDAR data and compared the results with and without IMU synchronisation<\/p>\n \u25cfThe result without IMU showed a latency of 50 to 60 milliseconds, while the result with IMU showed a latency of less than 10 microseconds, confirming that it is possible to output a position 5000 times faster and it’s almost ignorable for any practical use cases<\/p>\n \u5b9f\u7528\u4e0a\u30ec\u30a4\u30c6\u30f3\u30b7\u30fc\u3092\u307b\u307c\u7121\u8996\u3067\u304d\u308b\u30bc\u30ed\u30e9\u30b0\u30fb\u30c8\u30e9\u30c3\u30ad\u30f3\u30b0\uff08Zerolag tracking\uff09\u306fKudan\u306eGrandSLAM\u306e\u91cd\u8981\u306a\u7279\u5fb4\u3067\u3059\u3002 \u3053\u306e\u30dd\u30b9\u30c8\u3067\u306f\u305d\u306e\u6982\u8981\u306b\u3064\u3044\u3066\u3054\u7d39\u4ecb\u3057\u3066\u3044\u307e\u3059\u3002 What is Zerol […]<\/p>\n","protected":false},"author":1,"featured_media":118,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"spay_email":""},"categories":[3],"tags":[50,47,49,7,13,30,51],"acf":[],"aioseo_notices":[],"jetpack_featured_media_url":"https:\/\/www.kudan.io\/jp\/wp-content\/uploads\/sites\/3\/2020\/05\/Screen-Shot-2020-05-23-at-15.11.01.png","_links":{"self":[{"href":"https:\/\/www.kudan.io\/jp\/wp-json\/wp\/v2\/posts\/88"}],"collection":[{"href":"https:\/\/www.kudan.io\/jp\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.kudan.io\/jp\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.kudan.io\/jp\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.kudan.io\/jp\/wp-json\/wp\/v2\/comments?post=88"}],"version-history":[{"count":11,"href":"https:\/\/www.kudan.io\/jp\/wp-json\/wp\/v2\/posts\/88\/revisions"}],"predecessor-version":[{"id":117,"href":"https:\/\/www.kudan.io\/jp\/wp-json\/wp\/v2\/posts\/88\/revisions\/117"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.kudan.io\/jp\/wp-json\/wp\/v2\/media\/118"}],"wp:attachment":[{"href":"https:\/\/www.kudan.io\/jp\/wp-json\/wp\/v2\/media?parent=88"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.kudan.io\/jp\/wp-json\/wp\/v2\/categories?post=88"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.kudan.io\/jp\/wp-json\/wp\/v2\/tags?post=88"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}![\"\"](\"https:\/\/www.kudan.io\/jp\/wp-content\/uploads\/sites\/3\/2020\/05\/Figure_1.png\")
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